Infinite Edge Partition Models for Overlapping Community Detection and Link Prediction
A hierarchical gamma process infinite edge partition model is proposed to factorize the binary adjacency matrix of an unweighted undirected relational network under a Bernoulli-Poisson link. The model describes both homophily and stochastic equivalence, and is scalable to big sparse networks by focusing its computation on pairs of linked nodes. It can not only discover overlapping communities and inter-community interactions, but also predict missing edges. A simplified version omitting inter-community interactions is also provided and we reveal its interesting connections to existing models. The number of communities is automatically inferred in a nonparametric Bayesian manner, and efficient inference via Gibbs sampling is derived using novel data augmentation techniques. Experimental results on four real networks demonstrate the models' scalability and state-of-the-art performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Community DetectionData AugmentationLink PredictionSimilar Papers 제목 키워드 기반
Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters
We propose a new framework called Ego-Splitting for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple o…
ClusteringCommunity Detectiongraph partitioningOverlapping community detection in networks based on link partitioning and partitioning around medoids
In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are o…
Community DetectionAn Enhanced Multi-Objective Biogeography-Based Optimization for Overlapping Community Detection in Social Networks with Node Attributes
Community detection is one of the most important and interesting issues in social network analysis. In recent years, simultaneous considering of nodes' attributes and topological structures of social networks in the proc…
Community DetectionA Flexible Fitness Function for Community Detection in Complex Networks
Most community detection algorithms from the literature work as optimization tools that minimize a given \textit{fitness function}, while assuming that each node belongs to a single community. Since there is no hard conc…
Community DetectionIEDC: An Integrated Approach for Overlapping and Non-overlapping Community Detection
Community detection is a task of fundamental importance in social network analysis that can be used in a variety of knowledge-based domains. While there exist many works on community detection based on connectivity struc…
Community Detection