research-article

OFFER: A Motif Dimensional Framework for Network Representation Learning

Authors:

Shuo Yu,

Feng Xia,

Jin Xu,

Zhikui Chen,

Ivan LeeAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 3349 - 3352

https://doi.org/10.1145/3340531.3417446

Published: 19 October 2020 Publication History

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Abstract

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.

Supplementary Material

MP4 File (3340531.3417446.mp4)

This is the video of demo paper entitled "OFFER: A Motif Dimensional Framework for Network Representation Learning" for CIKM2020. In this 5-minute video, we introduced the research content of our paper in detail from four parts: "Introduction", "Model Design", "Experiment", and "Discussion". Specifically, we first introduce the relations between multivariate relationships and network motifs. Then, we introduce our two proposed metrics, i.e., node motif degree and edge motif degree. We then introduce the experimental results of link prediction task and clustering task. Finally, we conclude the paper.

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Yuan XChen JWang YChen AHuang YZhao WYu S(2024)Semantic-Enhanced Knowledge Graph CompletionMathematics10.3390/math1203045012:3(450)Online publication date: 31-Jan-2024
https://doi.org/10.3390/math12030450
Sheng JZhang YWang BChang Y(2024)MGATs: Motif-Based Graph Attention NetworksMathematics10.3390/math1202029312:2(293)Online publication date: 16-Jan-2024
https://doi.org/10.3390/math12020293
Chen NQiu TZhou XZhang SSi WOliver Wu D(2024)A Distributed Co-Evolutionary Optimization Method With Motif for Large-Scale IoT RobustnessIEEE/ACM Transactions on Networking10.1109/TNET.2024.340776932:5(4085-4098)Online publication date: Oct-2024
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Index Terms

OFFER: A Motif Dimensional Framework for Network Representation Learning
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
2. Mathematics of computing

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Published In

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

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CIKM '20

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CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

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Cited By

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Sheng JZhang YWang BChang Y(2024)MGATs: Motif-Based Graph Attention NetworksMathematics10.3390/math1202029312:2(293)Online publication date: 16-Jan-2024
https://doi.org/10.3390/math12020293
Chen NQiu TZhou XZhang SSi WOliver Wu D(2024)A Distributed Co-Evolutionary Optimization Method With Motif for Large-Scale IoT RobustnessIEEE/ACM Transactions on Networking10.1109/TNET.2024.340776932:5(4085-4098)Online publication date: Oct-2024
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Yu SXia FWang YLi SFebrinanto FChetty M(2024)PANDORA: Deep Graph Learning Based COVID-19 Infection Risk Level ForecastingIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.322967111:1(717-730)Online publication date: Feb-2024
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Wang QCao HLi XChang KCheng R(2024)From Motif to Path: Connectivity and Homophily2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00227(2751-2764)Online publication date: 13-May-2024
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Sun CZhang SQiu LJing C(2024)Normalized auto-encoder based on biased walk for network representation learningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107265130(107265)Online publication date: Apr-2024
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Ding FLuo NYu SWang TXia F(2023)MEGA: Explaining Graph Neural Networks with Network Motifs2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191684(1-9)Online publication date: 18-Jun-2023
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Yan RBao PShen HLi X(2023)Learning node representation via Motif CoarseningKnowledge-Based Systems10.1016/j.knosys.2023.110821278(110821)Online publication date: Oct-2023
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Huang HDing FZhang FWang YPeng CShehzad ALei QCong LYu S(2023)Knowledge Graph Completion via Subgraph Topology AugmentationSocial Media Processing10.1007/978-981-99-7596-9_2(14-29)Online publication date: 15-Nov-2023
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