A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the underlying cluster structure in two stages. The first stage embeds the nodes of the graph in real space, …
Graph InfoClust learns node representations by capturing cluster-level information, improving graph mining tasks.
CAGNN learns graph embeddings without labels by clustering and refining graph topology.
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
Local graph clustering improves with noisy labels, enhancing accuracy and performance.
Biological and social systems consist of myriad interacting units. The interactions can be represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications s…
Novel GNN method for semi-supervised clustering of signed networks.
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods that, based on the dissimilarity structure, output hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter. Our construction of hierarchical cl…
New method clusters multilayer graphs with missing nodes.
A new model clusters network nodes based on relative edge weights.
A graph clustering method that moves nodes to highest-degree neighbors.
Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being able to cluster within a network is important, there are emerging needs to be ab…
New framework minimizes interference and selection bias in network A/B testing.
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network. However, it does not account for interactions of time varying intensity between clusters. The extension of the SBM developed in this paper addresses this shortcoming through a…
Efficient algorithm for self-directed learning of convex clusters on graphs.
Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network…
Directed graphs have asymmetric connections, yet the current graph clustering methodologies cannot identify the potentially global structure of these asymmetries. We give a spectral algorithm called di-sim that builds on a dual measure of similarity that correspond to how a node (i) sends and (ii) receives edges. Using…
This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a re…
This work tackles community detection in networks with node attributes, achieving exact recovery.
New algorithms cluster nodes in SBM graphs faster and more accurately.
New algorithm improves hypergraph clustering for unbalanced communities.
The paper examines node2vec embeddings for community detection in networks.
Paper proposes a method to improve graph clustering by integrating node textual metadata with node signals in GGMs.
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our constructi…
The paper corrects for node degree in spectral clustering using random walk Laplacian.
SASE improves attributed graph clustering for large graphs with linear time and space complexity.
Graph clustering remains challenging for GNNs, but a new method improves performance.
Study shows neural ODEs generalize well on synthetic graphs but struggle with degree heterogeneity and clustering.
We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently c…
Spectral clustering adapted for mixed data types without preprocessing.
Consistent spectral clustering with fairness constraints on representation graphs.
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that graph convolution is effective in combining structural and content information, and several recent methods based on it have achieved promisin…
Model clusters networks and their communities simultaneously.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people who communicate with each other, are one example. Communities or clusters of highly connected actors form an essential feature in the structure of several emp…
SPARC tackles cold-start nodes in graphs by using spectral embeddings.
New spectral clustering method for graphs with uneven node degrees.
A new model detects complex network communities using node attributes.
Spectral clustering is widely used to partition graphs into distinct modules or communities. Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs. We propose a new spectral partitioning method that exploi…
DCMAP optimizes clustering in Bayesian Networks with dependent costs.
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or community detection), and even subnetwork-state-sequence identificati…
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This pape…
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theo…
We derive an efficient method to perform clustering of nodes in Gaussian graphical models directly from sample data. Nodes are clustered based on the similarity of their network neighborhoods, with edge weights defined by partial correlations. In the limited-data scenario, where the covariance matrix would be rank-defi…
TD-GEN generates graphs using tree decomposition, improving efficiency and performance.
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step framewo…