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The aim of this workshop called Advances in managing and mining large evolving graphs (LEG) is to bring together active scholars and practitioners of dynamic graphs. Graph models and algorithms are ubiquitous of a large number of application domains, ranging from transportation to social networks, semantic web, or data mining. However, many applications require graph models that are time dependent. For example, applications related to urban mobility analysis employ a graph structure of the underlying road network. Indeed, the nature of such networks are spatiotemporal. Therefore, the time a moving object takes to cross a path segment typically depends on the starting instant of time. So, we call time-dependent graphs, the graphs that have this spatiotemporal feature.

Important dates:

  • Paper Submission Deadline: June 25, 2019 (Extended)
  • Author Notification: July 20, 2019
  • Camera Ready Deadline: July 26, 2019
  • Workshop: TBA

Useful links:

Accepted papers

Full papers

  • Souâad Boudebza, Rémy Cazabet, Omar Nouali and Faiçal Azouaou. Detecting Stable Communities in Link Streams at Multiple Temporal Scales. (pdf)
  • Tiphaine Viard and Raphaël Fournier-S'Niehotta. Encoding temporal and structural information in machine learning models for recommendation (pdf)
  • Lauranne Coppens, Jonathan De Venter, Sandra Mitrovic and Jochen De Weerdt. A comparative study of community detection techniques for large evolving graphs (pdf)
  • Sedigheh Mahdavi, Shima Khoshraftar and Aijun An. Dynamic Joint Variational Graph Autoencoders (pdf)
  • Ying Yin, Jianpeng Zhang, Yulong Pei, Xiaotao Cheng and Lixin Ji. MHDNE: Network Embedding Based on Multivariate Hawkes Process (pdf)
  • Christopher Rost, Andreas Thor, Philip Fritzsche, Kevin Gomez and Erhard Rahm. Evolution Analysis of Large Graphs with Gradoop (pdf)

    • Short papers

  • Zoltan Miklos, Mickaël Foursov, Franklin Lia, Ian Jeantet and David Gross-Amblard. Understanding the evolution of science: analyzing evolving term co-occurrence graphs with spectral techniques (pdf)

    • Posters

  • Kevin Dalleau, Miguel Couceiro and Malika Smail-Tabbone. Clustering graphs using random trees (pdf)
  • Roxane Jouseau and Frederic Andres. A Behavioral Focused Method for Community Extraction (pdf)
  • Abdelkader Ouali, Nyoman Juniarta, Amedeo Napoli and Bernard Maigret. A Feature Selection Method based on Tree Decomposition of Correlation Graph (pdf)

Final program

10:00-10:30: Poster session 1+ break

10:30-10:40: Welcome session

10:40-11:30: Invited talk 1: Danai Koutra, Title: Efficient Structural Embeddings in Large Time-varying Networks, Computer Science and Engineering University of Michigan

11:30-12:30: Session 1
11:30-11:50: Ying Yin, Jianpeng Zhang, Yulong Pei, Xiaotao Cheng and Lixin Ji. MHDNE: Network Embedding Based on Multivariate Hawkes Process (pdf)
11:50-12:10: Tiphaine Viard and Raphaël Fournier-S'Niehotta. Encoding temporal and structural information in machine learning models for recommendation (pdf)
12:10-12:30: Lauranne Coppens, Jonathan De Venter, Sandra Mitrovic and Jochen De Weerdt. A comparative study of community detection techniques for large evolving graphs (pdf)

Lunch break

14:00-15:30: Session 2
14:00-14:20: Sedigheh Mahdavi, Shima Khoshraftar and Aijun An. Dynamic Joint Variational Graph Autoencoders (pdf)
14:20-14:40: Souâad Boudebza, Rémy Cazabet, Omar Nouali and Faiçal Azouaou. Detecting Stable Communities in Link Streams at Multiple Temporal Scales. (pdf)
14:40-15:00: Christopher Rost, Andreas Thor, Philip Fritzsche, Kevin Gomez and Erhard Rahm. Evolution Analysis of Large Graphs with Gradoop (pdf)
15:00-15:15: Zoltan Miklos, Mickaël Foursov, Franklin Lia, Ian Jeantet and David Gross-Amblard. Understanding the evolution of science: analyzing evolving term co-occurrence graphs with spectral techniques (short paper) (pdf)

15:15 - 16:30: Poster session 2 + break

16:30-17:30: Discussion and concluding remarks