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:
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.
Carnegie Mellon University
Univ. of Illinois at Urbana-Champaign
University of Southern California (ISI)
University of California San Diego
Michigan State University
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:
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:
All papers will be peer reviewed, single-blinded. We welcome many kinds of papers, such as, but not limited to:
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 firstname.lastname@example.org To receive updates about the current and future workshops and the other related news, please join the Mailing List, or follow the Twitter Account.
Paper Submission Open:
Sep 1, 2017
Paper Submission Deadline:
Nov 23, 2017
Author Notification: Dec 20, 2017
Camera Ready: Jan 20, 2018
Workshop: Feb 9, 2018
University of Southern California (ISI)
University of California Los angeles
University of Notre Dame
Nesreen Ahmed (Intel Research Labs)
Yuxiao Dong (Microsoft Research)
Amir Ghasemian (University of Colorado Boulder)
Srijan Kumar (Stanford University)
Julian McAuley (University of California, San Diego)
Fred Morstatter (University of Southern California)
Maximilian Nickel (Facebook AI Research)
Evangelos Papalexakis (University of California Riverside)
Ali Pinar (Sandia National Laboratories)
Arti Ramesh (Binghamton University)
Neil Shah (Carnegie Mellon University)
Chuan Shi (Beijing Uni. of Posts & Telecommunications)
Jiliang Tang (Michigan State University)
Elena Zheleva (University of Illinois at Chicago)