International Workshop on
Heterogeneous Networks Analysis and Mining
Held in conjunction with WSDM'18
Feb 9, 2018 - Los Angeles, California, USA, 2018
Call for Papers


Graphs naturally represent a host of processes, including interactions between people on social or communication networks, links between webpages on the World Wide Web, protein interactions in biological networks, movement in transportation networks, electricity delivery in smart energy grids, relations in bibliographic data, and many others. In such scenarios, graphs that model real-world networks are typically heterogeneous, multi-modal, and multi-relational. In the era of big data, as more varieties of interconnected structured and semi-structured data are becoming available, the importance of leveraging this heterogeneous and multi-relational nature of networks in being able to effectively mine and learn this kind of data is becoming more evident. The objective of this workshop is to bring together researchers from a variety of related areas, and discuss commonalities and differences in challenges faced, survey some of the different approaches, and provide a forum to present and learn about some of the most cutting-edge research in this area. As an outcome, we expect participants to walk away with a better sense of the variety of different methods and tools available for heterogenous network mining and analysis, and an appreciation for some of the interesting emerging applications, as well as the challenges that accompany these applications
There are many challenges involved in effectively mining and learning from this kind of data, including:

  • Understanding the different techniques applicable, including heterogeneous graph mining algorithms, graphical models, latent variable models, matrix factorization methods and more.
  • Dealing with the heterogeneity of the data.
  • The common need for information integration and alignment.
  • Handling dynamic and changing data.
  • Addressing each of these issues at scale.

Traditionally, a number of subareas have contributed to this space: communities in graph mining, learning from structured data, statistical relational learning, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science.

Keynote Speakers

Keynote speakers will be announced soon!

Call for Papers

This workshop is a forum for exchanging ideas and methods for heterogeneous networks analysis and mining, developing new common understandings of the problems at hand, sharing of data sets where applicable, and leveraging existing knowledge from different disciplines. The goal is to bring together researchers from academia, industry, and government, to create a forum for discussing recent advances in this area. In doing so, we aim to better understand the overarching principles and the limitations of our current methods and to inspire research on new algorithms and techniques for heterogeneous networks analysis and mining.

To reflect the broad scope of work on heterogeneous networks analysis and mining, we encourage submissions that span the spectrum from theoretical analysis to algorithms and implementation, to applications and empirical studies is various domains. The need for analysis and learning methods that go beyond mining simple graphs is emerging in many disciplines and are referred to with different names depending on the type of data augmenting the simple graph.

Topics of interest include, but are not limited to:

  • Heterogeneous Information Networks
  • Multi-Relational Networks
  • Signed Networks
  • Attributed Networks
  • Aligned Networks
  • Multigraphs
  • Multidimensional Networks
  • Multilayer Networks
  • Complex Networks
  • Multimodal Networks

Heterogenous networks are becoming the key component in many emerging applications and data-mining and graph-mining related tasks. Some of the related research areas and tasks related to heterogeneous networks include:

  • Link and relationship strength prediction
  • Clustering and community detection and formation modeling
  • Learning to rank in information networks
  • Similarity measures and relationship extraction
  • Applications to modeling of weblogs, social media, social networks, medical networks, and the semantic web
  • Statistical relational learning
  • Tensor factorization
  • Network-based classification
  • Hybrid recommender systems
  • Information fusion
  • Network evolution and dynamic networks

All papers will be peer reviewed, single-blinded. We welcome many kinds of papers, such as, but not limited to:

  • Novel research papers
  • Demo papers
  • Work-in-progress papers
  • Visionary papers (white papers)
  • Appraisal papers of existing methods and tools (e.g., lessons learned)
  • Relevant work that has been previously published
  • Work that will be presented at the main conference of WSDM

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions.
Submissions must be in PDF, no more than 8 pages long — shorter papers are welcome — and formatted according to the standard double-column ACM Proceedings Style.
The accepted papers will be published on the workshop’s website and will not be considered archival for resubmission purposes.
Authors whose papers are accepted to the workshop will have the opportunity to participate in a spotlight and poster session, and some set may also be chosen for oral presentation. For paper submission, please proceed to the submission website.

Please send enquiries to

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Important Dates


Paper Submission Open: Sep 1, 2017

Paper Submission Deadline: Nov 20, 2017

Author Notification: Dec 14, 2017

Camera Ready: Jan 1, 2018

Workshop: Feb 9, 2018

Workshop Organizers

Shobeir Fakhraei

Research Scientist
USC Information Sciences Institute

Yanen Li

Research Scientist
Snap Inc.

Yizhou Sun

Assistant Professor
University of California Los angeles

Tim Weninger

Assistant Professor
University of Notre Dame

Program Committee