arXiv subjects

Ryan DeWolfe

Publications and source records attributed to Ryan DeWolfe.

7 recordsLinked to original sources

arXiv2026

Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding

Leveraging non-linear dimension reduction techniques, we remove the low dimension constraint from node embedding and propose COVE, an explainable high dimensional embedding that, when reduced to low dimension with UMAP, slightly increases performance on clustering and link prediction tasks. The embedding is inspired by neural embedding methods that use co-occurrence on a random walk as an indication of similarity, and is closely related to a diffusion process. Extending on recent community detection benchmarks, we find that a COVE UMAP HDBSCAN pipeline performs similarly to the popular Louvain algorithm.

cs.LG↗

arXiv2026

A Pragmatic Method for Comparing Clusterings with Overlaps and Outliers

Clustering algorithms are an essential part of the unsupervised data science ecosystem, and extrinsic evaluation of clustering algorithms requires a method for comparing the detected clustering to a ground truth clustering. In a general setting, the detected and ground truth clusterings may have outliers (objects belonging to no cluster), overlapping clusters (objects may belong to more than one cluster), or both, but methods for comparing these clusterings are currently undeveloped. In this note, we define a pragmatic similarity measure for comparing clusterings with overlaps and outliers, show that it has several desirable properties, and experimentally confirm that it is not subject to several common biases afflicting other clustering comparison measures.

cs.LG↗

arXiv2025

Hierarchical Single-Linkage Clustering for Community Detection with Overlaps and Outliers

Most community detection approaches make very strong assumptions about communities in the data, such as every vertex must belong to exactly one community (the communities form a partition). For vector data, Hierarchical Density Based Spatial Clustering for Applications with Noise (HDBSCAN) has emerged as a leading clustering algorithm that allows for outlier points that do not belong to any cluster. The first step in HDBSCAN is to redefine the distance between vectors in such a way that single-linkage clustering is effective and robust to noise. Many community detection algorithms start with a similar step that attempts to increase the weight of edges between similar nodes and decrease weights of noisy edges. In this paper, we apply the hierarchical single-linkage clustering algorithm from HDBSCAN to a variety of node/edge similarity scores to see if there is an algorithm that can effectively detect clusters while allowing for outliers. In experiments on synthetic and real world data sets, we find that no single method is optimal for every type of graph, but the admirable performance indicates that hierarchical single-linkage clustering is a viable paradigm for graph clustering.

cs.SI↗

arXiv2025

The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+$o^2$)

The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of the ABCD model, and its generalization to include outliers (ABCD+$o$), and introduce another variant that allows for overlapping communities, ABCD+$o^2$.

cs.SI↗

arXiv2025

Detecting Patterns of Interaction in Temporal Hypergraphs via Edge Clustering

Finding densely connected subsets of vertices in an unsupervised setting, called clustering or community detection, is one of the fundamental problems in network science. The edge clustering approach instead detects communities by clustering the edges of the graph and then assigning a vertex to a community if it has at least one edge in that community, thereby allowing for overlapping clusters of vertices. We apply the idea behind edge clustering to temporal hypergraphs, an extension of a graph where a single edge can contain any number of vertices and each edge has a timestamp. Extending to hypergraphs allows for many different patterns of interaction between edges, and by defining a suitable structural similarity function, our edge clustering algorithm can find clusters of these patterns. We test the algorithm with three structural similarity functions on a large collaboration hypergraph, and find intuitive cluster structures that could prove useful for downstream tasks.

cs.SI↗

arXiv2025

Improving community detection via community association strength scores

Community detection methods play a central role in understanding complex networks by revealing highly connected subsets of entities. However, most community detection algorithms generate partitions of the nodes, thus (i) forcing every node to be part of a community and (ii) ignoring the possibility that some nodes may be part of multiple communities. In our work, we investigate three simple community association strength (CAS) scores and their usefulness as post-processing tools given some partition of the nodes. We show that these measures can be used to improve node partitions, detect outlier nodes (not part of any community), and help find nodes with multiple community memberships.

cs.SI↗

arXiv2023

Random Models for Fuzzy Clustering Similarity Measures

The Adjusted Rand Index (ARI) is a widely used method for comparing hard clusterings, but requires a choice of random model that is often left implicit. Several recent works have extended the Rand Index to fuzzy clusterings, but the assumptions of the most common random model is difficult to justify in fuzzy settings. We propose a single framework for computing the ARI with three random models that are intuitive and explainable for both hard and fuzzy clusterings, along with the benefit of lower computational complexity. The theory and assumptions of the proposed models are contrasted with the existing permutation model. Computations on synthetic and benchmark data show that each model has distinct behaviour, meaning that accurate model selection is important for the reliability of results.

stat.ML↗