Topic

While much of network sciences research has focused on studying static networks, almost all real networks are dynamic in nature. Those networks exhibit structural changes over time. The importance of studying and modelling such networks is evident from its increasing presence : telecom call data graphs, communication networks, social (friends') networks, biological networks, language, etc. Recently, research areas for studying the topology, evolution and applications of complex evolving networks have gained more attention. In general, network modeling has long drawn on the tradition of social network analysis and graph theory. In the last decade, there is a growth in class of dynamic network models, from Erdos-Renyi model to Logit models, p*-models, and Markov random graphs. These network models, exemplified by the preferential attachment model, are widely popular in statistical physics and computer science research communities. Most of the these works deal with identifying properties in a single snapshot of a large network, or in a very small number of snapshots. The network properties include heavy tails for in-degree and out-degree distributions, communities, small-world phenomena, etc. However, in cases of availability of information about network evolution over long periods and/or at smaller granularity of time periods, for example telecom call data graphs, these methods are not very effective. Leskovec et al. in their KDD 2005 paper, point out that though models like preferential attachment are good at generating networks that match static "snapshots" of real-world networks, they do not appropriately model how real-world networks change over time. Since these models have network's topology at the core, they are well-suited for capturing the essential features of static networks (or time aggregated snapshots of dynamic networks) only. However, in many cases the interactions among the nodes are not only dynamic but also defined on a very short time scale. This has led to the development of activity driven network models. Even with these advancements, dynamic network analysis research area lacks a wholistic view of methods and models proposed in different domains, w.r.t properties and processes/activities, analyzed and modeled respectively. It is also critical to review each of the techniques for various problems and application domains, which can help in coming up with unified frameworks that can potentially address the shortcomings.

In this workshop titled Networks over Time, we solicit original papers (including work in progress) on each of the topics on or before Jan 15th 2013 to narrow the research gap in dynamic network analysis.

Topics of interest include(but are not limited to):

(1) Analyzing the commonality and differences between techniques in various domains.
(2) Comparing the techniques w.r.t the network properties analyzed
(3) Application domains from biology to social sciences
(4) Problem domains from anomaly detection, link prediction, dynamic graph clustering to community detection.
(5) Large-scale dynamic network modeling - since most of these dynamic networks are large in nature, it also requires mechanisms to handle large networks and perform computations in a distributed fashion.
(6) (Large-scale)Simulation systems that enable high-fidelity simulations.

This one day workshop will include a number of paper and position presentations and two/three talks from invited speakers.

Invited Speakers

Sanjay Chawla, The University of Sydney

Submission Instructions
Each submitted paper should include an abstract up to 200 words. The submissions must not be longer than 12 single-spaced pages with 10pt font size. Authors are strongly encouraged to use Springer LNCS/LNAI manuscript submission guidelines (http://www.springer.com/computer/lncs/lncs+authors?SGWID=0-40209-0-0-0) for their initial submissions. All papers must be submitted electronically through EasyChair Conference Management Service (https://www.easychair.org/account/signin.cgi?conf=pakddnot2013) in PDF format only. Submitting a paper to the workshop means that if the paper were accepted, at least one author will attend the workshop to present the paper.

Organisers
Rickard Cöster , Ericsson Research Sweden
Niloy Ganguly, IIT Kharagpur
Balaraman Ravindran, IIT Madras
Subramanian Shivashankar, Ericsson Research India
Samarth Swarup, Virginia Bioinformatics Institute, Virginia Tech

Advisory Committee
Noshir Contractor, Northwestern University
Madhav Marathe, Virginia Bioinformatics Institute, Virginia Tech
Martin Svensson, Ericsson Research Sweden
Anand Varadarajan, Ericsson Research India

Program Committee
Andrea Baronchelli, Northeastern Univ
Indrajit Bhattacharya, IBM Research
Jean-Charles Delvenne, Université catholique de Louvain
Peter Holmes, Umeå University, Sweden
V. S. Anil Kumar, Virginia Tech
Arun S. Maiya, Institute for Defense Analyses
Bivas Mitra, Samsung Research India
Sriraam Natarajan, Wake Forest University Baptist Medical Center
Sandhya Prabhakaran, University of Basel
S. S. Ravi, University at Albany, SUNY
Fernando Peruani, Université de Nice - Sophia Antipolis
Ashish Tendulkar, TIFR
Jierui(Jerry) Xie, Oracle