Keyword Aware Influential Community Search in Large Attributed Graphs

TL;DR

This paper presents a new keyword-aware influential community search method in large attributed graphs, using semantic similarity and influence measures to find cohesive, influential communities matching user queries.

Contribution

It introduces the KICQ framework with a word-embedding based similarity model and influence measure, along with two efficient algorithms for large-scale community search.

Findings

Semantic community search alleviates keyword limitations.

Proposed influence measure considers cohesiveness and influence.

Algorithms demonstrate effectiveness and efficiency in experiments.

Abstract

We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches.