Abstract

Networks (i.e., graphs) are often collected from multiple sources and platforms, such as social networks extracted from multiple online platforms, team-specific collaboration networks within an organization, and inter-dependent infrastructure networks, etc. Such networks from different sources form the multi-networks, which can exhibit the unique patterns that are invisible if we mine the individual network separately. However, compared with single-network mining, multi-network mining is still under-explored due to its unique challenges. First (multi-network models), networks under different circumstances can be modeled into a variety of models. How to properly build multi-network models from the complex data? Second (multi-network mining algorithms), it is often nontrivial to either extend single-network mining algorithms to multi-networks or design new algorithms. How to develop effective and efficient mining algorithms on multi-networks? The objectives of this tutorial are to: (1) comprehensively review the existing multi-network models, (2) elaborate the techniques in multi-network mining with a special focus on recent advances, and (3) elucidate open challenges and future research directions. We believe this tutorial could be beneficial to various application domains, and attract researchers and practitioners from data mining as well as other interdisciplinary fields.

Descriptions

Detailed descriptions can be found here.

Outline

• Introduction (20 minutes)

  • Motivation and background

  • Key challenges

  • Traditional and related settings

• Part I: Multi-network models (40 minutes)

  • Multiplex networks and multi-view networks

  • Multi-layered networks

  • Hypergraphs

  • Network of networks

• Part II: Multi-network mining algorithms (90 minutes)

  • Multi-network ranking

  • Multi-network classification

  • Multi-network clustering

  • (Hyper-)link prediction

  • Multi-network association

  • Multi-network embedding

• Part III: Multi-network Future directions (30 minutes)

  • Novel multi-network models

  • Advanced multi-network mining algorithms

  • Multi-network applications

Slides

Speakers' Bio

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