# Comparative Analysis of Embedding Methods for Non-Euclidean Dissimilarity Data: A Comprehensive Evaluation of Topolow

#### Omid Arhami

#### 2025-08-04

# 1 Abstract

This vignette presents a comprehensive comparative analysis of embedding methods for non-Euclidean dissimilarity data, with particular focus on the Topolow algorithm. We evaluate three fundamentally different approaches: Topolow (force-directed optimization), Classical Multidimensional Scaling (eigenvalue decomposition), and Iterative MDS (STRESS minimization) across two distinct types of non-Euclidean datasets. Our analysis demonstrates the relative strengths and limitations of each method when dealing with incomplete, non-metric dissimilarity matrices that violate fundamental assumptions of Euclidean geometry.

# 2 Introduction

The embedding of high-dimensional or non-metric dissimilarity data into low-dimensional Euclidean spaces is a fundamental problem in computational statistics, bioinformatics, and machine learning. While classical methods such as Multidimensional Scaling (MDS) provide theoretically optimal solutions under ideal conditions, real-world datasets often exhibit characteristics that challenge these assumptions: sparse measurements, non-Euclidean geometry, and threshold-censored observations.

The Topolow algorithm represents a novel approach to this problem, utilizing physics-inspired force-directed optimization to handle these challenging data characteristics naturally. This vignette provides a rigorous comparative evaluation of Topolow against established methods using controlled synthetic datasets that exhibit known non-Euclidean properties.

## 2.1 Methodological Framework

We evaluate three distinct embedding approaches:

1. Topolow: A force-directed method that models objects as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing measurements)
2. Classical MDS: The mathematically optimal eigenvalue-based solution that provides the theoretical best-case Euclidean approximation
3. Iterative MDS: A gradient-based STRESS minimization approach that uses stochastic optimization similar to Topolow

This three-method comparison allows us to understand both the theoretical limits of embedding accuracy and the practical differences in optimization stability between deterministic and stochastic methods.

    # Enhanced statistical testing with effect sizes
    enhanced_statistical_comparison <- function(topolow_errors, other_errors, method_name) {
      valid_topolow <- topolow_errors[!is.na(topolow_errors)]
      valid_other <- other_errors[!is.na(other_errors)]

      if(length(valid_other) < 2 || length(valid_topolow) < 2) {
        cat("Insufficient data for statistical comparison with", method_name, "\n")
        return(NULL)
      }

      # T-test
      t_result <- t.test(valid_topolow, valid_other)

      # Effect size (Cohen's d)
      pooled_sd <- sqrt(((length(valid_topolow) - 1) * var(valid_topolow) +
                         (length(valid_other) - 1) * var(valid_other)) /
                        (length(valid_topolow) + length(valid_other) - 2))

      cohens_d <- (mean(valid_topolow) - mean(valid_other)) / pooled_sd

      cat("\n--- Statistical Comparison (Topolow vs", method_name, ") ---\n")
      cat("- Welch's t-statistic:", round(t_result$statistic, 3), "\n")
      cat("- p-value:", format(t_result$p.value, scientific = TRUE, digits = 3), "\n")
      cat("- Cohen's d (effect size):", round(cohens_d, 3), "\n")
      cat("- Effect size interpretation:",
          if(abs(cohens_d) < 0.2) "Negligible"
          else if(abs(cohens_d) < 0.5) "Small"
          else if(abs(cohens_d) < 0.8) "Medium"
          else "Large", "\n")

      return(list(t_test = t_result, cohens_d = cohens_d))
    }

    # Calculate data quality metrics
    calculate_data_quality_metrics <- function(distance_matrix) {
      total_possible <- nrow(distance_matrix) * (nrow(distance_matrix) - 1) / 2
      total_available <- sum(!is.na(distance_matrix[upper.tri(distance_matrix)]))
      completeness <- total_available / total_possible

      available_distances <- distance_matrix[!is.na(distance_matrix)]

      metrics <- list(
        completeness = completeness,
        n_objects = nrow(distance_matrix),
        n_available_distances = total_available,
        distance_range = range(available_distances),
        distance_mean = mean(available_distances),
        distance_sd = sd(available_distances),
        distance_cv = sd(available_distances) / mean(available_distances)
      )
      return(metrics)
    }

    # Coordinate validation
    validate_coordinates <- function(coords, method_name, n_objects, target_dims) {
      if(is.null(coords)) {
        cat("WARNING:", method_name, "returned NULL coordinates\n")
        return(FALSE)
      }
      if(any(!is.finite(coords))) {
        cat("WARNING:", method_name, "has non-finite coordinates\n")
        return(FALSE)
      }
      if(nrow(coords) != n_objects || ncol(coords) != target_dims) {
        cat("WARNING:", method_name, "has incorrect dimensions\n")
        return(FALSE)
      }
      return(TRUE)
    }

    # Missing data imputation
    improved_missing_data_imputation <- function(dist_matrix) {
      available_distances <- dist_matrix[!is.na(dist_matrix)]
      if(length(available_distances) == 0) {
        median_distance <- 1.0
      } else {
        median_distance <- median(available_distances, na.rm = TRUE)
      }
      dist_matrix[is.na(dist_matrix)] <- median_distance
      return(list(matrix = dist_matrix, imputation_value = median_distance))
    }

# 3 Data Generation Methodologies

We employ two distinct data generation methods to create non-Euclidean datasets with controlled properties, allowing for systematic evaluation of embedding performance under different types of geometric violations.

## 3.1 Method 1: Synthesized non-Euclidean Space

This approach creates non-Euclidean data by systematically distorting a high-dimensional clustered dataset through non-linear transformations and asymmetric noise injection.

    #' Generate Non-Euclidean Data by Distorting Clustered Points
    #'
    #' Creates non-Euclidean data through systematic distortion of clustered high-dimensional points.
    #' This method is particularly effective for testing robustness to violations of the triangle inequality.
    #'
    #' @param n_objects Number of objects to generate
    #' @param initial_dims Dimensionality of the initial latent space
    #' @param n_clusters Number of clusters to form
    #' @param noise_factor Magnitude of asymmetric noise to add
    #' @param missing_fraction Proportion of distances to set to NA
    #' @return List containing complete and incomplete distance matrices
    generate_distorted_euclidean_data <- function(n_objects = 50,
                                                  initial_dims = 10,
                                                  n_clusters = 8,
                                                  noise_factor = 0.3,
                                                  missing_fraction = 0.30) {

      object_names <- paste0("Object_", sprintf("%02d", 1:n_objects))

      # Generate structured high-dimensional coordinates
      cluster_size <- n_objects %/% n_clusters
      cluster_centers <- matrix(rnorm(n_clusters * initial_dims, mean = 0, sd = 4),
                               nrow = n_clusters, ncol = initial_dims)
      initial_coords <- matrix(0, nrow = n_objects, ncol = initial_dims)
      rownames(initial_coords) <- object_names

      for(i in 1:n_objects) {
        cluster_id <- ((i - 1) %/% cluster_size) + 1
        if(cluster_id > n_clusters) cluster_id <- n_clusters
        initial_coords[i, ] <- cluster_centers[cluster_id, ] +
                              rnorm(initial_dims, mean = 0, sd = 1.5)
      }

      # Calculate foundational Euclidean distances
      euclidean_distances <- as.matrix(dist(initial_coords, method = "euclidean"))

      # Apply non-linear transformations
      dist_quantiles <- quantile(euclidean_distances[upper.tri(euclidean_distances)],
                                c(0.33, 0.66))
      transform_1 <- euclidean_distances
      transform_1[euclidean_distances <= dist_quantiles[1]] <-
        euclidean_distances[euclidean_distances <= dist_quantiles[1]]^1.3
      transform_1[euclidean_distances > dist_quantiles[1] &
                 euclidean_distances <= dist_quantiles[2]] <-
        euclidean_distances[euclidean_distances > dist_quantiles[1] &
                           euclidean_distances <= dist_quantiles[2]]^1.6
      transform_1[euclidean_distances > dist_quantiles[2]] <-
        euclidean_distances[euclidean_distances > dist_quantiles[2]]^1.8

      # Add asymmetric noise
      asymmetric_noise <- transform_1 * noise_factor *
                         matrix(runif(n_objects^2, -1, 1), nrow = n_objects)
      asymmetric_noise <- (asymmetric_noise + t(asymmetric_noise)) / 2
      transform_2 <- transform_1 + asymmetric_noise
      transform_2[transform_2 < 0] <- 0.01

      # Create final non-Euclidean distance matrix
      complete_non_euclidean_distances <- transform_2
      diag(complete_non_euclidean_distances) <- 0

      # Introduce missing values
      total_unique_pairs <- n_objects * (n_objects - 1) / 2
      target_missing_pairs <- round(total_unique_pairs * missing_fraction)

      upper_tri_indices <- which(upper.tri(complete_non_euclidean_distances), arr.ind = TRUE)
      missing_pair_indices <- sample(nrow(upper_tri_indices), target_missing_pairs)

      incomplete_distances <- complete_non_euclidean_distances
      incomplete_distances[upper_tri_indices[missing_pair_indices,]] <- NA
      incomplete_distances[upper_tri_indices[missing_pair_indices, c(2,1)]] <- NA

      return(list(
        complete_distances = complete_non_euclidean_distances,
        incomplete_distances = incomplete_distances,
        object_names = object_names,
        method = "Synthesized non-Euclidean"
      ))
    }

## 3.2 Method 2: Swiss Roll Manifold

This approach generates data points on a 2D “Swiss Roll” manifold embedded in 3D space, where true distances are geodesic (along the manifold surface) and thus inherently non-Euclidean from a 3D perspective. x = t * cos(t), y = height, z = t * sin(t), t = 1.5 * pi * (1 + 2runif)

Marsland, “Machine Learning: An Algorithmic Perspective”, 2nd edition, Chapter 6, 2014

    #' Generate Non-Euclidean Data from a Swiss Roll Manifold
    #'
    #' Creates data points on a Swiss Roll manifold where geodesic distances
    #' are inherently non-Euclidean. Includes "tunneling" effects common in real-world manifold data.
    #'
    #' @param n_objects Number of points on the manifold
    #' @param noise Standard deviation of Gaussian noise added to points
    #' @param tunnel_fraction Fraction of distances to replace with Euclidean shortcuts
    #' @param missing_fraction Proportion of final distances to set to NA
    #' @return List containing complete and incomplete distance matrices
    generate_swiss_roll_data <- function(n_objects = 50,
                                         noise = 0.05,
                                         tunnel_fraction = 0.05,
                                         missing_fraction = 0.30) {
      # Check if required packages are available
      if(!requireNamespace("RANN", quietly = TRUE)) {
        stop("RANN package is required for this function. Please install it.")
      }
      if(!requireNamespace("igraph", quietly = TRUE)) {
        stop("igraph package is required for this function. Please install it.")
      }

      # Generate points on the Swiss Roll
      t <- 1.5 * pi * (1 + 2 * runif(n_objects))
      height <- 21 * runif(n_objects)

      x <- t * cos(t)
      y <- height
      z <- t * sin(t)

      points_3d <- data.frame(x = x, y = y, z = z) + rnorm(n_objects * 3, sd = noise)
      object_names <- paste0("Object_", sprintf("%02d", 1:n_objects))
      rownames(points_3d) <- object_names

      # Calculate geodesic distances via k-NN graph
      if(requireNamespace("RANN", quietly = TRUE)) {
        knn_graph <- RANN::nn2(points_3d, k = min(10, n_objects-1))$nn.idx
        adj_matrix <- matrix(0, n_objects, n_objects)
        for (i in 1:n_objects) {
          for (j_idx in 2:min(10, n_objects)) {
            if(j_idx <= ncol(knn_graph)) {
              j <- knn_graph[i, j_idx]
              dist_ij <- dist(rbind(points_3d[i,], points_3d[j,]))
              adj_matrix[i, j] <- dist_ij
              adj_matrix[j, i] <- dist_ij
            }
          }
        }

        g <- igraph::graph_from_adjacency_matrix(adj_matrix, mode = "undirected", weighted = TRUE)
        geodesic_distances <- igraph::distances(g)
        geodesic_distances[is.infinite(geodesic_distances)] <-
          max(geodesic_distances[is.finite(geodesic_distances)]) * 1.5
      } else {
        # Fallback to Euclidean if RANN not available
        geodesic_distances <- as.matrix(dist(points_3d))
        warning("RANN package not available. Using Euclidean distances as approximation.")
      }

      # Introduce "tunnels" or "short-circuits"
      euclidean_distances <- as.matrix(dist(points_3d))
      n_tunnels <- round(tunnel_fraction * n_objects * (n_objects - 1) / 2)

      upper_tri_indices <- which(upper.tri(geodesic_distances), arr.ind = TRUE)
      tunnel_indices <- sample(nrow(upper_tri_indices), min(n_tunnels, nrow(upper_tri_indices)))

      complete_distances <- geodesic_distances
      if(length(tunnel_indices) > 0) {
        for (k in tunnel_indices) {
          i <- upper_tri_indices[k, 1]
          j <- upper_tri_indices[k, 2]
          complete_distances[i, j] <- complete_distances[j, i] <- euclidean_distances[i, j]
        }
      }
      diag(complete_distances) <- 0

      # Introduce missing values
      total_unique_pairs <- n_objects * (n_objects - 1) / 2
      target_missing_pairs <- round(total_unique_pairs * missing_fraction)

      missing_pair_indices <- sample(nrow(upper_tri_indices),
                                    min(target_missing_pairs, nrow(upper_tri_indices)))

      incomplete_distances <- complete_distances
      if(length(missing_pair_indices) > 0) {
        incomplete_distances[upper_tri_indices[missing_pair_indices,]] <- NA
        incomplete_distances[upper_tri_indices[missing_pair_indices, c(2,1)]] <- NA
      }

      rownames(complete_distances) <- object_names
      colnames(complete_distances) <- object_names
      rownames(incomplete_distances) <- object_names
      colnames(incomplete_distances) <- object_names

      return(list(
        complete_distances = complete_distances,
        incomplete_distances = incomplete_distances,
        object_names = object_names,
        method = "Swiss Roll Manifold"
      ))
    }

# 4 Experimental Design and Analysis Pipeline

Our analysis follows a standardized pipeline for each dataset type:

1. Data Generation with controlled non-Euclidean characteristics
2. Geometric Assessment via eigenvalue analysis of the Gram matrix
3. Parameter Optimization using the Euclidify automated workflow
4. Three-Method Comparison with multiple runs for stochastic methods
5. Performance Evaluation and statistical analysis
6. Distance Preservation Assessment

    #' Comprehensive analysis pipeline for embedding method comparison
    #'
    #' @param data_gen_func Function to generate the dataset
    #' @param dataset_name Name for the dataset (for labeling)
    #' @param n_objects Number of objects in the dataset
    #' @param n_runs Number of runs for stochastic methods
    run_comprehensive_analysis <- function(data_gen_func, dataset_name, n_objects = 50, n_runs = 50) {

      cat("=== ANALYSIS FOR", toupper(dataset_name), "===\n")

      # Step 1: Data Generation
      cat("\n1. Generating", dataset_name, "dataset...\n")
      data_gen_output <- data_gen_func(n_objects = n_objects, missing_fraction = 0.3)

      complete_distances_for_evaluation <- data_gen_output$complete_distances
      incomplete_distances_for_embedding <- data_gen_output$incomplete_distances
      object_names <- data_gen_output$object_names

      actual_missing_percentage <- sum(is.na(incomplete_distances_for_embedding)) /
                                  (n_objects * (n_objects-1)) * 100

      data_quality <- calculate_data_quality_metrics(incomplete_distances_for_embedding)

      cat("Generated dataset with", n_objects, "objects and",
          round(actual_missing_percentage, 1), "% missing values\n")

      # Step 2: Assess Non-Euclidean Character
      cat("\n2. Assessing non-Euclidean character...\n")

      D_squared <- complete_distances_for_evaluation^2
      n <- nrow(D_squared)
      J <- diag(n) - (1/n) * matrix(1, n, n)
      B <- -0.5 * J %*% D_squared %*% J
      eigenvals <- eigen(B, symmetric = TRUE)$values

      numerical_tolerance <- 1e-12
      positive_eigenvals <- eigenvals[eigenvals > numerical_tolerance]
      negative_eigenvals <- eigenvals[eigenvals < -numerical_tolerance]

      total_variance <- sum(abs(eigenvals))
      negative_variance <- sum(abs(negative_eigenvals))
      positive_variance <- sum(positive_eigenvals)

      if(positive_variance > 0) {
        deviation_score <- negative_variance / positive_variance
      } else {
        deviation_score <- 1.0
      }

      negative_variance_fraction <- negative_variance / total_variance

      cumulative_positive_variance <- cumsum(positive_eigenvals) / positive_variance
      dims_for_90_percent <- which(cumulative_positive_variance >= 0.90)[1]
      if(is.na(dims_for_90_percent)) dims_for_90_percent <- length(positive_eigenvals)

      cat("Non-Euclidean deviation score:", round(deviation_score, 4), "\n")
      cat("Dimensions for 90% variance:", dims_for_90_percent, "\n")

      # Step 3: Parameter Optimization using Euclidify
      cat("\n3. Running automated parameter optimization...\n")

      # Create temporary directory for optimization
      temp_dir <- tempfile()
      dir.create(temp_dir, recursive = TRUE)

      euclidify_results <- tryCatch({
        Euclidify(
          dissimilarity_matrix = incomplete_distances_for_embedding,
          output_dir = temp_dir,
          ndim_range = c(2, min(10, dims_for_90_percent + 3)),
          n_initial_samples = 20,  # Reduced for vignette speed
          n_adaptive_samples = 40,  # Reduced for vignette speed
          folds = 20,
          mapping_max_iter = 300,  # Reduced for speed
          clean_intermediate = TRUE,
          verbose = "off",
          fallback_to_defaults = TRUE,
          save_results = FALSE
        )
      }, error = function(e) {
        cat("Euclidify failed, using default parameters\n")
        NULL
      })

      # Clean up temp directory
      unlink(temp_dir, recursive = TRUE)

      if(!is.null(euclidify_results)) {
        optimal_params <- euclidify_results$optimal_params
        euclidify_positions <- euclidify_results$positions
        target_dims <- optimal_params$ndim
        cat("Optimal parameters found - dimensions:", target_dims, "\n")
      } else {
        # Fallback parameters
        target_dims <- max(2, min(5, dims_for_90_percent))
        optimal_params <- list(
          ndim = target_dims,
          k0 = 5.0,
          cooling_rate = 0.01,
          c_repulsion = 0.02
        )
        euclidify_positions <- NULL
        cat("Using fallback parameters - dimensions:", target_dims, "\n")
      }

      # Step 4: Three-Method Comparison
      cat("\n4. Running three-method comparison...\n")

      # Initialize storage
      topolow_results <- vector("list", n_runs)
      iterative_mds_results <- vector("list", n_runs)
      classical_mds_result <- NULL

      topolow_errors <- numeric(n_runs)
      iterative_mds_errors <- numeric(n_runs)
      classical_mds_error <- NA

      # Topolow runs
      cat("Running Topolow embeddings...\n")
      for(i in 1:n_runs) {
        if(i == 1 && !is.null(euclidify_positions)) {
          # Use Euclidify result for first run
          topolow_coords <- euclidify_positions
        } else {
          # Run new embedding
          topolow_result <- tryCatch({
            euclidean_embedding(
              dissimilarity_matrix = incomplete_distances_for_embedding,
              ndim = optimal_params$ndim,
              mapping_max_iter = 300,
              k0 = optimal_params$k0,
              cooling_rate = optimal_params$cooling_rate,
              c_repulsion = optimal_params$c_repulsion,
              relative_epsilon = 1e-6,
              convergence_counter = 3,
              verbose = FALSE
            )
          }, error = function(e) NULL)

          if(!is.null(topolow_result)) {
            topolow_coords <- topolow_result$positions
          } else {
            topolow_coords <- NULL
          }
        }

        if(!is.null(topolow_coords)) {
          topolow_coords <- topolow_coords[order(row.names(topolow_coords)), ]

          if(validate_coordinates(topolow_coords, "Topolow", n_objects, target_dims)) {
            topolow_coords <- scale(topolow_coords, center = TRUE, scale = FALSE)
            topolow_results[[i]] <- topolow_coords

            embedded_distances <- as.matrix(dist(topolow_coords))
            rownames(embedded_distances) <- rownames(topolow_coords)
            colnames(embedded_distances) <- rownames(topolow_coords)

            valid_mask <- !is.na(complete_distances_for_evaluation)
            distance_errors <- abs(complete_distances_for_evaluation[valid_mask] -
                                 embedded_distances[valid_mask])
            topolow_errors[i] <- sum(distance_errors)
          } else {
            topolow_results[[i]] <- NULL
            topolow_errors[i] <- NA
          }
        } else {
          topolow_results[[i]] <- NULL
          topolow_errors[i] <- NA
        }
      }

      # Classical MDS
      cat("Running Classical MDS...\n")
      imputation_result <- improved_missing_data_imputation(incomplete_distances_for_embedding)
      classical_mds_matrix <- imputation_result$matrix

      tryCatch({
        classical_mds_result_raw <- cmdscale(classical_mds_matrix, k = target_dims, eig = TRUE)
        classical_mds_coords <- classical_mds_result_raw$points
        rownames(classical_mds_coords) <- object_names
        classical_mds_coords <- classical_mds_coords[order(row.names(classical_mds_coords)), ]

        if(validate_coordinates(classical_mds_coords, "Classical MDS", n_objects, target_dims)) {
          classical_mds_coords <- scale(classical_mds_coords, center = TRUE, scale = FALSE)
          classical_mds_result <- classical_mds_coords

          embedded_distances <- as.matrix(dist(classical_mds_coords))
          rownames(embedded_distances) <- rownames(classical_mds_coords)
          colnames(embedded_distances) <- rownames(classical_mds_coords)

          valid_mask <- !is.na(complete_distances_for_evaluation)
          distance_errors <- abs(complete_distances_for_evaluation[valid_mask] -
                               embedded_distances[valid_mask])
          classical_mds_error <- sum(distance_errors)
        }
      }, error = function(e) {
        cat("Classical MDS failed:", e$message, "\n")
      })

      # Iterative MDS
      cat("Running Iterative MDS...\n")
      for(i in 1:n_runs) {
        tryCatch({
          if(requireNamespace("smacof", quietly = TRUE)) {
            max_dist <- max(classical_mds_matrix, na.rm = TRUE)
            scaled_matrix <- classical_mds_matrix / max_dist

            iterative_mds_result_raw <- smacof::smacofSym(
              delta = scaled_matrix,
              ndim = target_dims,
              type = "interval",
              init = "torgerson",
              verbose = FALSE,
              itmax = 1000,
              eps = 1e-5
            )

            iterative_mds_coords <- iterative_mds_result_raw$conf
            current_max_dist <- max(dist(iterative_mds_coords))
            if(current_max_dist > 0) {
              scale_factor <- max_dist / current_max_dist
              iterative_mds_coords <- iterative_mds_coords * scale_factor
            }
          } else {
            # Fallback to isoMDS
            iterative_mds_result_raw <- MASS::isoMDS(classical_mds_matrix, k = target_dims, trace = FALSE)
            iterative_mds_coords <- iterative_mds_result_raw$points
          }

          rownames(iterative_mds_coords) <- object_names
          iterative_mds_coords <- iterative_mds_coords[order(row.names(iterative_mds_coords)), ]

          if(validate_coordinates(iterative_mds_coords, "Iterative MDS", n_objects, target_dims)) {
            iterative_mds_coords <- scale(iterative_mds_coords, center = TRUE, scale = FALSE)
            iterative_mds_results[[i]] <- iterative_mds_coords

            embedded_distances <- as.matrix(dist(iterative_mds_coords))
            rownames(embedded_distances) <- rownames(iterative_mds_coords)
            colnames(embedded_distances) <- rownames(iterative_mds_coords)

            valid_mask <- !is.na(complete_distances_for_evaluation)
            distance_errors <- abs(complete_distances_for_evaluation[valid_mask] -
                                 embedded_distances[valid_mask])
            iterative_mds_errors[i] <- sum(distance_errors)
          } else {
            iterative_mds_results[[i]] <- NULL
            iterative_mds_errors[i] <- NA
          }

        }, error = function(e) {
          iterative_mds_results[[i]] <- NULL
          iterative_mds_errors[i] <- NA
        })
      }

      # Step 5: Compile Results
      valid_topolow_results <- !is.na(topolow_errors)
      valid_iterative_results <- !is.na(iterative_mds_errors)

      results <- list(
        dataset_name = dataset_name,
        data_characteristics = list(
          n_objects = n_objects,
          missing_percentage = actual_missing_percentage,
          deviation_score = deviation_score,
          dims_90_percent = dims_for_90_percent,
          negative_variance_fraction = negative_variance_fraction
        ),
        optimal_params = optimal_params,
        topolow_errors = topolow_errors,
        topolow_results = topolow_results,
        valid_topolow_results = valid_topolow_results,
        classical_mds_error = classical_mds_error,
        classical_mds_result = classical_mds_result,
        iterative_mds_errors = iterative_mds_errors,
        iterative_mds_results = iterative_mds_results,
        valid_iterative_results = valid_iterative_results,
        complete_distances = complete_distances_for_evaluation,
        incomplete_distances = incomplete_distances_for_embedding,
        target_dims = target_dims
      )

      return(results)
    }

# 5 Results: Synthesized non-Euclidean Data

We begin our analysis with the Synthesized non-Euclidean dataset, which represents a controlled deviation from metric properties through systematic transformations.

    # Run analysis for Synthesized non-Euclidean data
    distorted_results <- run_comprehensive_analysis(
      generate_distorted_euclidean_data,
      "Synthesized non-Euclidean",
      n_objects = 50,
      n_runs = 50
    )

    #> === Synthesized non-Euclidean DATA ANALYSIS SUMMARY ===
    #> Dataset characteristics:
    #> - Objects: 50
    #> - Missing data: 30 %
    #> - Non-Euclidean deviation score: 0.6034
    #> - Dimensions for 90% variance: 13
    #> - Target embedding dimensions: 3
    #> Performance Summary:
    #> - Topolow mean error: 115995.6 ± 414.62
    #> - Classical MDS error: 154109.3
    #> - Iterative MDS mean error: 263692 ± 0

## 5.1 Eigenvalue Spectrum Analysis - Synthesized non-Euclidean

## 5.2 Performance Comparison - Synthesized non-Euclidean

# 6 Results: Swiss Roll Manifold Data

Next, we analyze the Swiss Roll manifold dataset, which represents geodesic distances that are inherently non-Euclidean in the embedding space.

    # Run analysis for Swiss Roll data
    swiss_results <- run_comprehensive_analysis(
      generate_swiss_roll_data,
      "Swiss Roll Manifold",
      n_objects = 50,
      n_runs = 50
    )

    #> === SWISS ROLL MANIFOLD DATA ANALYSIS SUMMARY ===
    #> Dataset characteristics:
    #> - Objects: 50
    #> - Missing data: 30 %
    #> - Non-Euclidean deviation score: 0.2945
    #> - Dimensions for 90% variance: 9
    #> - Target embedding dimensions: 4
    #> Performance Summary:
    #> - Topolow mean error: 2871.67 ± 76.94
    #> - Classical MDS error: 5727.19
    #> - Iterative MDS mean error: 9628.46 ± 0

## 6.1 Eigenvalue Spectrum Analysis - Swiss Roll

## 6.2 Performance Comparison - Swiss Roll

# 7 Comparative Analysis Across Dataset Types

## 7.1 Combined Performance Analysis

    #>
    #> === COMBINED PERFORMANCE SUMMARY ===
    #> # A tibble: 6 × 8
    #>   Method        Dataset                   Method_Type   Count Mean_Error SD_Error Min_Error Max_Error
    #>   <chr>         <chr>                     <chr>         <int>      <dbl>    <dbl>     <dbl>     <dbl>
    #> 1 Classical MDS Swiss Roll                Deterministic     1      5727.     NA       5727.     5727.
    #> 2 Classical MDS Synthesized non-Euclidean Deterministic     1    154109.     NA     154109.   154109.
    #> 3 Iterative MDS Swiss Roll                Stochastic       50      9628.      0       9628.     9628.
    #> 4 Iterative MDS Synthesized non-Euclidean Stochastic       50    263692.      0     263692.   263692.
    #> 5 Topolow       Swiss Roll                Stochastic       50      2872.     76.9     2822.     3369.
    #> 6 Topolow       Synthesized non-Euclidean Stochastic       50    115996.    415.    115258.   116960.

## 7.2 Statistical Comparisons

    #>
    #> === STATISTICAL ANALYSIS ===
    #>
    #> Synthesized non-Euclidean Data:
    #>
    #> --- Statistical Comparison (Topolow vs Iterative MDS ) ---
    #> - Welch's t-statistic: -2518.874
    #> - p-value: 6.39e-127
    #> - Cohen's d (effect size): -503.775
    #> - Effect size interpretation: Large
    #> $t_test
    #>
    #>  Welch Two Sample t-test
    #>
    #> data:  valid_topolow and valid_other
    #> t = -2518.9, df = 49, p-value < 2.2e-16
    #> alternative hypothesis: true difference in means is not equal to 0
    #> 95 percent confidence interval:
    #>  -147814.3 -147578.6
    #> sample estimates:
    #> mean of x mean of y
    #>  115995.6  263692.0
    #>
    #>
    #> $cohens_d
    #> [1] -503.7748
    #>
    #> Swiss Roll Manifold Data:
    #>
    #> --- Statistical Comparison (Topolow vs Iterative MDS ) ---
    #> - Welch's t-statistic: -620.991
    #> - p-value: 4e-97
    #> - Cohen's d (effect size): -124.198
    #> - Effect size interpretation: Large
    #> $t_test
    #>
    #>  Welch Two Sample t-test
    #>
    #> data:  valid_topolow and valid_other
    #> t = -620.99, df = 49, p-value < 2.2e-16
    #> alternative hypothesis: true difference in means is not equal to 0
    #> 95 percent confidence interval:
    #>  -6778.653 -6734.922
    #> sample estimates:
    #> mean of x mean of y
    #>  2871.674  9628.462
    #>
    #>
    #> $cohens_d
    #> [1] -124.1981

## 7.3 Distance Preservation Analysis

    #>
    #> === DISTANCE PRESERVATION: SYNTHESIZED NON-EUCLIDEAN ===

    #>
    #> Distance Preservation Statistics:
    #>          Method                   Dataset Correlation R_squared
    #> 1       Topolow Synthesized non-Euclidean   0.8938058 0.7988889
    #> 2 Classical MDS Synthesized non-Euclidean   0.7988927 0.6382295
    #> 3 Iterative MDS Synthesized non-Euclidean   0.8059096 0.6494903
    #>
    #> === DISTANCE PRESERVATION: SWISS ROLL ===

    #>
    #> Distance Preservation Statistics:
    #>          Method    Dataset Correlation R_squared
    #> 1       Topolow Swiss Roll   0.9788604 0.9581677
    #> 2 Classical MDS Swiss Roll   0.9085225 0.8254131
    #> 3 Iterative MDS Swiss Roll   0.9042658 0.8176966

# 8 Discussion

## 8.1 Key Findings

Our comprehensive analysis reveals several important insights about the performance of embedding methods on non-Euclidean dissimilarity data:

### 8.1.1 1. Non-Euclidean Character Assessment

The eigenvalue spectrum analysis provides crucial context for interpreting method performance:

- Synthesized non-Euclidean Data: Shows deviation score of [run round(distorted_results\(data_characteristics\)deviation_score, 4) to see], indicating moderate non-Euclidean structure
- Swiss Roll Data: Shows deviation score of [run round(swiss_results\(data_characteristics\)deviation_score, 4) to see]`, reflecting the manifold’s inherent geometric properties

### 8.1.2 2. Method Performance Characteristics

Topolow consistently demonstrates the best performance across both datasets, with its force-directed approach naturally handling missing data without requiring imputation. The method’s physics-inspired optimization appears particularly well-suited for sparse, non-Euclidean datasets.

Classical MDS provides the theoretical baseline for Euclidean approximation but requires complete data matrices through imputation. Performance varies significantly based on the quality of imputation and the degree of non-Euclidean structure.

Iterative MDS shows no stochasticity in our tests due to the fixed optimal initialization. The method achieves performance comparable to classical MDS.

### 8.1.3 3. Distance Preservation Quality

The correlation analysis reveals how well each method preserves the original distance relationships. However, correlation is not a sufficient measure of accuracy and needs to be interpreted joint with measures of error such as MAE.

    #> Distance Preservation Summary (Correlation with Original Distances):
    #>          Method                   Dataset Correlation R_squared
    #> 1       Topolow Synthesized non-Euclidean   0.8938058 0.7988889
    #> 2 Classical MDS Synthesized non-Euclidean   0.7988927 0.6382295
    #> 3 Iterative MDS Synthesized non-Euclidean   0.8059096 0.6494903
    #> 4       Topolow                Swiss Roll   0.9788604 0.9581677
    #> 5 Classical MDS                Swiss Roll   0.9085225 0.8254131
    #> 6 Iterative MDS                Swiss Roll   0.9042658 0.8176966
    #>
    #> Mean Performance Across Dataset Types:
    #> # A tibble: 3 × 3
    #>   Method        Mean_Correlation Mean_R_squared
    #>   <chr>                    <dbl>          <dbl>
    #> 1 Classical MDS            0.854          0.732
    #> 2 Iterative MDS            0.855          0.734
    #> 3 Topolow                  0.936          0.879

## 8.2 Methodological Implications

### 8.2.1 For Sparse Data

The analysis demonstrates that Topolow’s approach of using spring networks to handle missing measurements provides a principled alternative to imputation-based methods. This is particularly relevant for:

- Biological assay data with systematic missingness
- Network analysis with incomplete edge information
- High-throughput screening data with threshold censoring

### 8.2.2 For Non-Euclidean Data

Different types of non-Euclidean structure pose varying challenges:

- Systematic distortions (as in our Synthesized non-Euclidean data) can often be well-approximated by optimization
- Manifold structure (as in Swiss Roll data) can be embedded into a metric space using the faithfull transformation or topolow

### 8.2.3 Parameter Optimization

The Euclidify wizard function automatically tunes parameters and optimizes the output coordinates, making it particularly valuable for non-expert users.

## 8.3 Limitations and Future Directions

### 8.3.1 Current Limitations

1. Computational Complexity: run-time of topolow scales poorly with dataset size, making parallel processing a bottleneck

### 8.3.2 Future Research Directions

1. Scalability Improvements: Development for large-scale applications
2. Adaptive Dimensionality: Dynamic adjustment of embedding dimensions during optimization
3. Domain-Specific Evaluation: Metrics tailored to specific application domains (e.g., geography, phylogenetics, proteomics, psycometrics, operations research, …)

# 9 Conclusions

This comprehensive evaluation demonstrates that the choice of embedding method significantly impacts results when dealing with non-Euclidean dissimilarity data. Key recommendations include:

## 9.1 For Practitioners

1. Use Euclidify for automated parameter optimization rather than manual tuning
2. Assess Euclidean deviation in your data using eigenvalue analysis before selecting methods
3. Evaluate distance preservation quality.

## 9.2 For Method Developers

1. Missing data handling should be considered a core feature, not an afterthought
2. Robustness to non-Euclidean structure varies significantly between approaches
3. Automated parameter optimization is essential for practical adoption
4. Comprehensive evaluation frameworks like this vignette are needed for informed model selection

## 9.3 Scientific Impact

The Topolow algorithm represents a meaningful contribution to the embedding literature by:

- Providing a principled approach to missing data that avoids imputation artifacts
- Demonstrating robustness across different types of non-Euclidean structure
- Offering automated optimization tools that make the method accessible to non-experts
- Establishing performance benchmarks against established methods

This analysis provides a rigorous foundation for understanding when and how to apply force-directed embedding methods for challenging dissimilarity data, contributing to the broader goal of extracting meaningful low-dimensional representations from complex high-dimensional relationships.

# 10 Session Information

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    #> Running under: macOS 15.4.1
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    #>
    #> attached base packages:
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    #> other attached packages:
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    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
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    #>
    #> other attached packages:
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