July 1, 2016

8:45 AM - 9:00 AM        Welcome & Opening Remarks by Akhil Arora,  Xerox Research Centre India.

9:00 AM - 10:00 AM      Keynote by Prof. Jure Leskovec, Assistant Professor, Stanford University

Keynote Details:

Title: Beyond nodes and edges: Multiresolution algorithms for network data

Abstract: Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuro, and social sciences. Present network algorithms are almost exclusively focusing on first-order, or edge-based, structures in networks. However, what is missing from the picture are methods for analyzing higher-order organization of complex networks. We present a generalized framework for a network clustering and classification based on higher-order network connectivity patterns. This framework allows for identifying rich higher-order clusters in networks. Our framework scales to networks with billions of edges and provides mathematical guarantees on the optimality of obtained clusters. We apply our framework to  networks from a variety of scientific domains with scales ranging from a few hundred to over one billion links.

Speaker Bio: Dr. Jure Leskovec is an Assistant Professor of Computer Science at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. He investigates problems that are motivated by large scale data, the Web and on-line media. He is also a Chief Scientist at Pinterest, where he is focusing on machine learning problems.

10:00 AM - 10:30 AM    Coffee Break

10:30 AM - 12:00 PM    Contributed Talks: (20 minutes each, including Q&A's)

    • Analyzing Extended Property Graphs with Apache Flink
      Martin Junghanns (University of Leipzig & ScaDS Dresden/Leipzig), André Petermann (University of Leipzig & ScaDS Dresden/Leipzig), Niklas Teichmann (University of Leipzig & ScaDS Dresden/Leipzig), Kevin Gómez (University of Leipzig & ScaDS Dresden/Leipzig) and Erhard Rahm (University of Leipzig & ScaDS Dresden/Leipzig).

    • Integer Programming Approach for Directed Minimum Spanning Tree Problem on Temporal Graphs
      Takuto Ikuta (The University of Tokyo) and Takuya Akiba (National Institute of Informatics, Tokyo)

    • NScaleSpark: Subgraph-centric Graph Analytics on Apache Spark
      Abdul Quamar (University of Maryland) and Amol Deshpande (University of Maryland)

    • Nepal: A Path Query Language for Communication Networks
      Theodore Johnson (AT&T Labs-Research), Yaron Kanza (AT&T Labs-Research), Laks Lakshmanan (University of British Columbia) and Vladislav Shkapenyuk (AT&T Labs-Research)

12:00 PM - 2:00 PM    Lunch Break

2:00 PM - 3:00 PM    Keynote by Prof. Laks V.S. Lakshmanan, Professor, University of British Columbia

Keynote Details:

Title: Viral Marketing 2.0 

Abstract: Over the last decade, there has been considerable excitement and research on the study and exploitation of the spread of information and influence over networks. Tremendous advances have been made on the prototypical problem of selecting a small number of seed users to activate over a social network such that the number of activated nodes in an expected sense is maximized, under several standard information diffusion models. Scalable heuristics, but more notably scalable approximation algorithms, have been developed in the recent years. Unfortunately, the state of the art has several shortcomings.

Firstly, most of the research has focused on a simplistic setting where one marketing campaign is active at a time. While there has been some work on modeling and optimizing for competing diffusions, the key role played by the network owner in a campaign has been overlooked. Secondly, the relationship and contract needed between the network owner and the advertisers is not captured. Thirdly, in real life, relationships between multiple campaigns may be more complex than just pure competition. Finally, most of the studies assume that the seeds must be chosen all at once before the campaign starts with no opportunity to observe the performance of seeds chosen earlier and course-correct as needed. We make a call to arms for opening up the framework of viral marketing to allow for more expressive business models and seed selection strategies, and present some  recent research from our group that addresses the modeling and computational challenges. 

Speaker Bio: Dr. Laks V.S. Lakshmanan is a Professor of Computer Science at University of British Columbia, Vancouver BC. His research covers a wide spectrum of topics including: data management and mining, advanced data models for novel applications, OLAP and data warehousing, data integration, data cleaning, semi-structured data and XML, information and social networks and social media, recommender systems, and personalization. 

3:00 PM - 3:30 PM    Coffee Break

3:30 PM - 5:00 PM    Invited Talks: (30 minutes each, including Q&A's)

    • Tracking the Conductance of Rapidly Evolving Topic-Subgraphs [VLDB'16]
      Sainyam Galhotra (UMass Amherst), Amitabha Bagchi (IIT Delhi), Srikanta Bedathur (IBM Research), Maya Ramanath (IIT Delhi) and Vidit Jain (American Express Big Data Labs)

    • From Competition to Complementarity: Comparative Influence Diffusion and Maximization [VLDB'16]
      Wei Lu (LinkedIn), Wei Chen (Microsoft Research Asia) and Laks Lakshmanan (University of British Columbia)

    • Storing and Analyzing Historical Graph Data at Scale [EDBT'16]
      Udayan Khurana (IBM T.J. Watson Research Center), Amol Deshpande (University of Maryland, College Park)