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ICDM 2023 Workshop 

1st IEEE ICDM Workshop on

Higher-Order Analytics of Large Graphs (HOALG)

Dec 4, 2023
Shanghai, China

ICDM 2023 and the HOALG workshop will take place in Shanghai, China.


About ICDM Workshop
Higher-Order Analytics of Large Graphs (HOALG)

Dec 4, 2023
09:00 am

The IEEE ICDM Workshop on Higher-Order Analytics of Large Graphs (HOALG) explores the challenges on high-order analytics of large graphs. Graphs are prominent in modeling the relationship of real-world entities, and its size is increased dramatically as the revolution of data collection endpoints. A motif, also known as a higher-order structure or graphlet, is considered as a fundamental "basic building block" of a large graph. Specifically, a motif is a small subgraph that appears frequently in a graph. A motif can help researchers to understand the significant, structural, or evolutionary design principles used to construct the large graphs. For example, a feed-forward loop motif is often used to study regulatory control mechanism in gene transcriptional networks. Hence, exact and approximate motif counting and discovery solutions have been developed intensively for different kinds of large graphs, e.g., heterogeneous information networks, typed graphs, uncertain graphs, dynamic and temporal graphs. New computing architectures and hardwares have been applied to make it more efficient and scalable while maintaining the mining effectiveness. Also, researchers used motifs extensively for higher-order graph analytics solutions, such as graph clustering, ranking, embedding, visualization, link prediction, recommendation, deep learning model design, fraud detection, community search, cliques and densest subgraph discovery. New insights or better analytic effectiveness are often obtained when enabling the higher-order semantics with the help of motifs


This workshop has a number of objectives:

  • Avenue for Presenting Research: Provide a forum for presenting research in this emerging area of motif discoveries and higher-order graph analytics.

  • Platform for Discussions: Provide a platform for researchers interested in this area to engage in discussions on how this emerging area could shape up in the future.

  • Cross-pollination: Encourage computer science researchers from different domains (e.g., data mining and management, computer systems, and artificial intelligence), to share their perspectives and visions on this area, and help computing researchers to realize potential for cross-disciplinary approaches in this area to eliminate any systemic blind spots.

The Conference
Call for Paper

Call for Paper

The IEEE ICDM Workshop on Higher-order Analytics of Large Graphs is inviting submissions that relates to data science for higher-order graph analytics.

Topics of interest include, but are not limited to:

  • Graph Analytics

  • Graph Visualization

  • Heterogeneous Data Mining

  • Distributed and Parallel Computing

  • Deep Learning

  • Social Network Analytics

  • Recommendation Systems

  • Web Analytics

  • Time-evolving Network Mining

  • Information Retrieval


All accepted paper will have to give a presentation at the 23th IEEE International Conference on Data Mining (ICDM) workshop day on Dec 4, 2023. Submissions should not exceed 8 pages plus 2 extra pages, formatted according to the IEEE 2-column format. All accepted workshop papers will be published in the ICDMW proceedings, published by the IEEE Computer Society Press.

All submissions will be reviewed according to the following criteria:

  • Originality of the research

  • Relevance to the workshop

  • Impact on our society

  • Soundness of the contribution

  • Review of related work

  • Presentation and language

All reviews will be single-blind.

Important dates

  • Sep 15, 2023: Paper submission deadline

  • Sep 24, 2023: Notification of acceptance

  • Oct 1, 2023: Submission of camera-ready version

  • Dec 4, 2023: Workshops date

Program Committee

Keynote Speaker

Parallel Lines





Department of Computer Science, University of Hong Kong, HKSAR, China


School of Computer Science, The Northwestern Polytechnical University, Xi'an, China

chenhao Ma_edited.jpg

School of Data Science, The Chinese University of Hong Kong, Shenzhen, China


Xiaodong Li

Department of Computer Science, University of Hong Kong, HKSAR, China


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