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Title:Meta-path analysis for community detection in heterogeneous graphs with bound truth labels
Author(s):Hasan, Aamir
Contributor(s):Vasudevan, Shobha
Subject(s):Community Detection
Heterogeneous Graphs
Machine Learning
Abstract:Community Detection in heterogeneous graphs has important applications in various fields such as sociology, biology and computer science. Algorithms that are currently used for community detection do not exploit information about the types of edges connecting two nodes and focus entirely on the topology of the graph. We apply the GeneMAPR algorithm that was developed to detect similarities between genes given an input set of known relations between the genes by training regression models using features extracted through meta-path analysis on the graph and generalize it for the purpose of community detection with the aid of ground truth labels. We performed experiments using MAPR to affirm that meta-path analysis can be used to detect communities in homogeneous and heterogeneous graphs. We also conclude that MAPR is able to learn the patterns between nodes provided in the sample set (ground truth labels) and can find similar nodes in the graph to detect meaningful clusters. Additionally, we also analyze MAPR's dependence on its input sample set(s) and connect it to the manner in which it detects communities. While MAPR does not perform as well as other community detection techniques that are based on modularity based edge-cutting, we learned that MAPR can provide important insights into how semantic communities relate to the topology of a graph.
Issue Date:2019-05
Date Available in IDEALS:2019-06-14

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