Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning
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Published:2023-12-16
Issue:24
Volume:13
Page:13306
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Li Ding1ORCID, Li Wenzhong2ORCID, Zhang Guoqiang3, Chen Yizhou2, Zhong Xu2, Lin Mingkai2, Lu Sanglu2
Affiliation:
1. School of Computer Science and Information Engineering, Hubei University, Wuhan 430062, China 2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 3. School of Information Science and Technology, Hainan Normal University, Haikou 571158, China
Abstract
In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, which is usually composed of a sequence of edges, where each edge is represented by two endpoints and a weight. As a result of its large volume and highly dynamic nature, several graph sketches were proposed for the purposes of summarizing large-scale graph streams and enabling fast query processing. By using a compact data structure with hash functions, the graph sketches sequentially store the edges. Nevertheless, the existing graph sketches suffer from low performance on graph query tasks as a result of unpredictable collisions between heavy edges and light edges. To store heavy edges and light edges, this paper introduces a novel learning-based Dichotomy Graph Sketch (DGS) mechanism that uses two separate graph sketches, a heavy sketch and a light sketch. During a graph stream session, DGS obtains heavy edges and light edges, and uses these edges as training samples for a deep neural network (DNN) based binary classifier. The DNN-based classifier is then used to determine whether the upcoming edges are heavy or not. We will store the edges that are classified as heavy edges in the heavy sketch, and those that are classified as light edges in the light sketch. By combining the learnable classifier and Dichotomy Graph Sketches, the proposed mechanism resolves the hashing collision problem in conventional graph sketches and significantly improves graph query accuracy. The DGS algorithm outperforms the state-of-the-art graph sketches in a variety of graph query tasks based on extensive experiments that were conducted on four real-world graph stream datasets.
Funder
National Natural Science Foundation of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference44 articles.
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