Graph-Based Node Finding in Big Complex Contextual Social Graphs

Keshou Wu,Junwen Lu,Guanfeng Liu

doi:10.1155/2020/7909826

Abstract

Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches. The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS. The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

Highlights

Graph pattern matching (GPM) is widely used in many applications, like computer vision [1], chemical structure [2], and social networks [3,4,5,6]
Exp-1: Effectiveness. is experiment is to investigate the effectiveness of our RN-Monte Carlo Tree Search (MCTS) by comparing the average ranking function values of the top-K matches based on different settings of parameters
We can see that the average ranking function values returned by MC-TAG-K are always less than that of B-MCTS and Recurrent Neural Network-Based Monte Carlo Tree Search (RN-MCTS)

Summary

Introduction

Graph pattern matching (GPM) is widely used in many applications, like computer vision [1], chemical structure [2], and social networks [3,4,5,6]. Based on GPM, many applications focus on finding matches of a specific pattern (query) node, rather than the entire set of graph matching, for example, expert recommendation [7, 8] and egocentric search [9]. Like crowd-sourcing travel [11] and social network based e-commerce [12], social queries often need to find matches of the topKP under requirements of social contexts, i.e., the social positions, the social trust, and the social relationships that have significant influence on collaborations and decision-making [13, 14].

Methods

Results

Conclusion