Social Network Analysis and Graph Algorithms Track
Call for Papers

Track chairs: (

  • David Gleich (Purdue University)
  • Danai Koutra (University of Michigan)
  • Jie Tang (Tsinghua University)

We invite research contributions to the Social Network Analysis and Graph Algorithms track at the 31st edition of the Web Conference series (formerly known as WWW), to be held online April 25-29, 2022, hosted by Lyon, France (

Web-based social networks have created new ways for people to interact with each other and produce, consume, and distribute online information. Studying these new types of interactions and data is an unprecedented opportunity to address both new and longstanding questions across and between a number of different fields of research. Due to their societal importance, these systems are now targets of fraud and disinformation campaigns. Moreover, the use of online data from these systems to guide decision-making processes gives rise to many ethical concerns including privacy, fairness, and transparency. The sheer size of data also creates ongoing challenges regarding storage, analysis, compression, and sensemaking.

We encourage submissions in all areas of graph theory and algorithms, graph mining, and social network analysis, and, more broadly, work that integrates ideas from data mining, machine learning, social sciences, and computer science theory. This track explicitly focuses on the investigation of graph-based techniques for social networks and other web data with the goal of developing new theories, models, and algorithms to make these systems more effective and efficient. Specific topics in the space span tasks, ethical impacts of algorithms, emerging data types, and applications.


  • Graph construction, reconstruction, network inference, graph identification
  • Sparsification, sketching, and compression of network data
  • Subgraph and motif discovery
  • Influence propagation, information diffusion, spreading and epidemics
  • Link prediction
  • Graph summarization and visual analytics
  • Succinct data structures for network-related data
  • Network representation learning and graph embeddings
  • Reinforcement learning and advanced machine learning for graphs
  • Graph neural networks
  • Self-supervised learning on graphs
  • Pre-trained models and zero-shot learning for networked data
  • Causal inference in relational data
  • Querying and indexing algorithms for massive graphs

Ethical impacts of algorithms:

  • Privacy-preserving graph algorithms
  • Explainable graph algorithms
  • Fairness, bias, and transparency of graph mining and learning algorithms
  • Adversarial attacks on network algorithms and graph neural networks

Data types:

  • Analysis of heterogeneous, signed, attributed, and labeled networks
  • Dynamic network analysis and algorithms for graph streams
  • Multi-relational graph analysis
  • Higher-order graph and network algorithms
  • Knowledge graph mining and learning-based reasoning
  • Location-aware social network analysis and mobility
  • Mining and learning in graphs with missing information and noise


  • Social media analysis through the lenses of networks
  • Social mining, social search, and social recommendation systems
  • Social reputation and trust management
  • Game theoretic and economic aspects on graphs and networks
  • Detecting, understanding, and combating misinformation and fake news
  • Fraud, spam, and malice detection in relational domains
  • Web-based applications of graph mining (e.g., in economics, sociology)

Submission guidelines, relevant dates, and important policies can be found at