2021 IEEE 37th International Conference on Data Engineering (ICDE) | 2021
Selective Edge Shedding in Large Graphs Under Resource Constraints
Abstract
With the rapid development of the information age, many complex systems can be modeled as graphs. However, the unprecedented growth of data makes it extremely difficult for everyday users to process and mine very large graphs, given their limited computing resources such as personal computers and laptops. To address this challenge, we propose selective edge shedding. By estimating the original graph information from the reduced graph, it can accelerate graph algorithms and queries.In this paper, we propose two vertex-degree preserving edge shedding methods, the core of which are to maintain the expected vertex degree, so as to capture the basic characteristics of the network. Both methods allow users to control the size of the reduced graph based on the computing resource constraint. The experimental results show that the methods proposed in this paper can achieve up to 65% higher accuracy on graph analysis tasks compared to the competitive method, while consuming only 26%-57% running time, which fully demonstrates the advantages of the methods proposed in this work.