research-article

Assessing and ranking structural correlations in graphs

Authors:

Xifeng YanAuthors Info & Claims

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

Pages 937 - 948

https://doi.org/10.1145/1989323.1989421

Published: 12 June 2011 Publication History

Abstract

Real-life graphs not only have nodes and edges, but also have events taking place, e.g., product sales in social networks and virus infection in communication networks. Among different events, some exhibit strong correlation with the network structure, while others do not. Such structural correlation will shed light on viral influence existing in the corresponding network. Unfortunately, the traditional association mining concept is not applicable in graphs since it only works on homogeneous datasets like transactions and baskets.

We propose a novel measure for assessing such structural correlations in heterogeneous graph datasets with events. The measure applies hitting time to aggregate the proximity among nodes that have the same event. In order to calculate the correlation scores for many events in a large network, we develop a scalable framework, called gScore, using sampling and approximation. By comparing to the situation where events are randomly distributed in the same network, our method is able to discover events that are highly correlated with the graph structure. gScore is scalable and was successfully applied to the co-author DBLP network and social networks extracted from TaoBao.com, the largest online shopping network in China, with many interesting discoveries.

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Cited By

Yang XTu BLi QLi JPlaza A(2024)Graph Evolution-Based Vertex Extraction for Hyperspectral Anomaly DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330327335:12(17372-17386)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3303273
Tu BYang XHe WLi JPlaza A(2024)Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain AnalysisIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322716735:6(8358-8372)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3227167
Guo QLin DWang SWong RLin W(2024)Efficient Algorithms for Group Hitting Probability Queries on Large GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3349164(1-13)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3349164
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Index Terms

Assessing and ranking structural correlations in graphs
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SIGMOD '11: Proceedings of the 2011 ACM SIGMOD International Conference on Management of data

June 2011

1364 pages

ISBN:9781450306614

DOI:10.1145/1989323

General Chair:
Timos Sellis
IMIS/RC Athena
,
Program Chair:
Renée J. Miller
University of Toronto
,
Publications Chairs:
Anastasios Kementsietsidis
IBM T.J. Watson Research Center
,
Yannis Velegrakis
University of Trento

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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Association for Computing Machinery

New York, NY, United States

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Published: 12 June 2011

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SIGMOD/PODS '11

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SIGMOD

SIGMOD/PODS '11: International Conference on Management of Data

June 12 - 16, 2011

Athens, Greece

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Yang XTu BLi QLi JPlaza A(2024)Graph Evolution-Based Vertex Extraction for Hyperspectral Anomaly DetectionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.330327335:12(17372-17386)Online publication date: Dec-2024
https://doi.org/10.1109/TNNLS.2023.3303273
Tu BYang XHe WLi JPlaza A(2024)Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain AnalysisIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322716735:6(8358-8372)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3227167
Guo QLin DWang SWong RLin W(2024)Efficient Algorithms for Group Hitting Probability Queries on Large GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3349164(1-13)Online publication date: 2024
https://doi.org/10.1109/TKDE.2023.3349164
Zhou YGong SYao FChen HYu SLiu PZhang YYu GYu J(2024)Fast Iterative Graph Computing with Updated Neighbor States2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00193(2449-2462)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00193
Gong STian CYin QWang ZYu SZhang YYu WGeng LFu CYu GZhou J(2024)Ingress: an automated incremental graph processing systemThe VLDB Journal10.1007/s00778-024-00838-z33:3(781-806)Online publication date: 20-Feb-2024
https://doi.org/10.1007/s00778-024-00838-z
Yu SGong SZhang YYu WYin QTian CTao QYan YYu GZhou J(2023)Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00212(2766-2779)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00212
Coscia M(2022)Generalized Euclidean Measure to Estimate Distances on Multilayer NetworksACM Transactions on Knowledge Discovery from Data10.1145/352939616:6(1-22)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3529396
Kang SLee KShin K(2022)Personalized Graph Summarization: Formulation, Scalable Algorithms, and Applications2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00219(2319-2332)Online publication date: May-2022
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Bian YNi JCheng WZhang X(2022)The multi-walker chain and its application in local community detectionKnowledge and Information Systems10.1007/s10115-018-1247-160:3(1663-1691)Online publication date: 10-Mar-2022
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Gong STian CYin QYu WZhang YGeng LYu SYu GZhou J(2021)Automating incremental graph processing with flexible memoizationProceedings of the VLDB Endowment10.14778/3461535.346155014:9(1613-1625)Online publication date: 22-Oct-2021
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