SPECIAL TRACK ON COMPLEX NETWORKS

Analytics over complex networks is pervasive across science and engineering, e.g., social networks, communication networks, biological networks, knowledge graphs, and so forth. Very often these networks are dynamic, evolving over time as connections are made and broken and as nodes appear and disappear. While a deep foundation has been developed in the past two decades on analytics on real-world complex networks, relatively little progress has been made on scalable analytics as these networks evolve. There is a growing community of researchers, dispersed across Artificial Intelligence, Data Mining, Data Management, Complex Systems, and other research disciplines which are turning their attention to the many open challenges in the context of evolving networks.

The objective of the special track on Complex Networks is to bring together researchers from different disciplines in this emerging area, providing a forum for discussion of recent advances in analytics on evolving networks, and to offer an opportunity for researchers and practitioners to identify and discuss recent advances and new promising research directions.

TOPICS OF INTEREST

We aim to attract researchers with an interest in analytics on complex networks.
A non-exhaustive list of topics includes:

• Temporal and streaming networks
• Statistical and structural properties and models of evolving networks
• Sampling from evolving networks
• Predictive modeling on evolving networks
• Pattern mining, e.g., graph partitioning, community mining, and motif discovery
• Maintaining time-evolving models
• Anomaly detection, change detection
• Visual analytics
• Evaluation frameworks
• Incorporating content and context in evolution analysis
• (Re-)Construction of heterogeneous evolving networks
• Applications, e.g., in social networks, Web, metabolic networks, biological networks, road networks, communication networks, co-authorship and collaboration networks, chemistry, brain networks, sensor networks

IMPORTANT DATES

Programme Committee

• Dick Epema. TU Delft, Netherlands.
• George Fletcher. TU Eindhoven, Netherlands.
• Alexandru Iosup. VU Amsterdam and TU Delft, Netherlands.
• Nelly Litvak. Universiteit Twente, Netherlands.
• Taro Takaguchi. NICT, Japan.
• Yinghui Wu. Washington State University, USA.
• Nikolay Yakovets. TU Eindhoven, Netherlands.

SUBMISSION PROCEDURE

The conference solicits regular contributions (5-10 pages) of original work and extended abstracts (2 pages max.) of original work or work that was recently accepted or published in a peer-reviewed machine-learning, data mining or other relevant journal or high level international conference. In the latter case, the publication reference should be clearly mentioned, and the abstracts should be checked mainly for relevance, rather than receive a full review.

We explicitly invite different kinds of contributions, including technical contributions, visionary papers, case studies, demos, benchmark real and synthetic datasets.

The program committee will decide which contributions are selected for an oral presentation, and which ones are presented during a poster session (with spotlight presentations). Preference of authors will be taken into account as well. If your submission falls under one of the special tracks, we encourage you to submit it there.

All submissions must be in English and must be submitted as PDF files through the EasyChair submission page.

Submissions should be formatted using the Benelearn 2017 LaTeX template. For detailed formatting instructions, please refer to the template files. The maximum abstract length is 2 pages (references not included).

All accepted contributions will be published in the conference proceedings and publicly available on the Benelearn website, but no copyright will be claimed.

At least one author of each accepted submission will be expected to attend and present their findings at the conference. The deadline for paper submission is March 27, 2017.

TRACK CHAIR