Muhammad Ali;Anwar Said;Iqra Safder;Saeed Ul Hassan;Naif Radi Aljohani;Mudassir Shabbir
{"title":"MSDGSD: A Scalable Graph Descriptor for Processing Large Graphs","authors":"Muhammad Ali;Anwar Said;Iqra Safder;Saeed Ul Hassan;Naif Radi Aljohani;Mudassir Shabbir","doi":"10.1109/TCSS.2023.3338691","DOIUrl":null,"url":null,"abstract":"Graph representation methods have recently become the de facto standard for downstream machine learning tasks on graph-structured data and have found numerous applications, e.g., drug discovery & development, recommendation, and forecasting. However, the existing methods are specially designed to work in a centralized environment, which limits their applicability to small or medium-sized graphs. In this work, we present a graph embedding method that extracts graph representations in a distributed environment with independent and parallel machines. The proposed method is built-upon the existing approach, distributed graph statistical distance (DGSD), to enhance the scalability on large graphs. The key innovation of our work lies in the proposition of a batching mechanism for client-server message passing, which reduces communication overhead during the computation of the distance matrix. In addition, we present a sampling approach for computing pairwise distances between the nodes to compute the desired graph embedding. Moreover, we systematically explore six distinct variations of a distributed graph embeddings and subsequently subject them to comprehensive evaluation. Our extensive evaluations on over 20 graph datasets and ten baseline methods demonstrate improved running time and comparative classification accuracy compared to state-of-the-art embedding techniques.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10379490/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Graph representation methods have recently become the de facto standard for downstream machine learning tasks on graph-structured data and have found numerous applications, e.g., drug discovery & development, recommendation, and forecasting. However, the existing methods are specially designed to work in a centralized environment, which limits their applicability to small or medium-sized graphs. In this work, we present a graph embedding method that extracts graph representations in a distributed environment with independent and parallel machines. The proposed method is built-upon the existing approach, distributed graph statistical distance (DGSD), to enhance the scalability on large graphs. The key innovation of our work lies in the proposition of a batching mechanism for client-server message passing, which reduces communication overhead during the computation of the distance matrix. In addition, we present a sampling approach for computing pairwise distances between the nodes to compute the desired graph embedding. Moreover, we systematically explore six distinct variations of a distributed graph embeddings and subsequently subject them to comprehensive evaluation. Our extensive evaluations on over 20 graph datasets and ten baseline methods demonstrate improved running time and comparative classification accuracy compared to state-of-the-art embedding techniques.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.