Community Detection
225 papers with code • 13 benchmarks • 11 datasets
Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes.
Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models
Libraries
Use these libraries to find Community Detection models and implementationsLatest papers
"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models
The misuse of large language models (LLMs) has garnered significant attention from the general public and LLM vendors.
Random Walk on Multiple Networks
To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case.
Boosting Multitask Learning on Graphs through Higher-Order Task Affinities
Lastly, we provide a theoretical analysis to show that under a planted block model of tasks on graphs, our affinity scores can provably separate tasks into groups.
Fast Maximum $k$-Plex Algorithms Parameterized by Small Degeneracy Gaps
We define a novel parameter of the input instance, $g_k(G)$, the gap between the degeneracy bound and the size of the maximum $k$-plex in the given graph, and present an exact algorithm parameterized by this $g_k(G)$, which has a worst-case running time polynomial in the size of the input graph and exponential in $g_k(G)$.
Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs
Clustering (or community detection) on multilayer graphs poses several additional complications with respect to standard graphs as different layers may be characterized by different structures and types of information.
Matrix tri-factorization over the tropical semiring
We show that triFastSTMF performs similarly to Fast-NMTF in terms of approximation and prediction performance when fitted on the whole network.
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Dataset Augmented by ChatGPT
Inspired by the latest ChatGPT and Toolformer models, we propose the Graph-ToolFormer (Graph Reasoning oriented Toolformer) framework to teach LLMs themselves with prompts augmented by ChatGPT to use external graph reasoning API tools.
Genetic Analysis of Prostate Cancer with Computer Science Methods
Metastatic prostate cancer is one of the most common cancers in men.
Effective Hierarchical Information Threading Using Network Community Detection
With the tremendous growth in the volume of information produced online every day (e. g. news articles), there is a need for automatic methods to identify related information about events as the events evolve over time (i. e., information threads).