SOTAVerified

Community Detection

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

Papers

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Showing 626–650 of 919 papers

TitleStatusHype
Towards Direct Comparison of Community Structures in Social Networks—0
Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks—0
Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs—0
Transient crosslinking kinetics optimize gene cluster interactions—0
Transition Network Analysis: A Novel Framework for Modeling, Visualizing, and Identifying the Temporal Patterns of Learners and Learning Processes—0
Two provably consistent divide and conquer clustering algorithms for large networks—0
TxAllo: Dynamic Transaction Allocation in Sharded Blockchain Systems—0
Uncertainty in GNN Learning Evaluations: A Comparison Between Measures for Quantifying Randomness in GNN Community Detection—0
Uncovering communities of pipelines in the task-fMRI analytical space—0
Uncovering the Local Hidden Community Structure in Social Networks—0
Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling—0
Understanding graph embedding methods and their applications—0
Understanding Opinions Towards Climate Change on Social Media—0
Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs—0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems—0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
Universality of Computational Lower Bounds for Submatrix Detection—0
Universality of the stochastic block model—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Unsupervised Community Detection with a Potts Model Hamiltonian, an Efficient Algorithmic Solution, and Applications in Digital Pathology—0
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss—0
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data—0
Using deep learning for community discovery in social networks—0
Using network metrics to explore the community structure that underlies movement patterns—0
Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GNNAccuracy-NE2—Unverified
2CommunityGANF1-score0.09—Unverified
3Ego-SplittingF1-score0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1EdMotNMI0.42—Unverified
2CDNMFNMI0.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CDNMFACC0.48—Unverified
#ModelMetricClaimedVerifiedStatus
1CommunityGANF1-Score0.15—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.56—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.69—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.68—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.57—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1Smooth GEMSEC 2Modularity0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1CDNMFACC0.67—Unverified