SOCIAL NETWORK ANALYSIS AND GRAPH ALGORITHMS
- Jussara Almeida (Federal University of Minas Gerais, Brazil)
- Paolo Boldi (Università degli Studi di Milano, Italy)
- Huawei Shen (Chinese Academy of Sciences, China)
We invite research contributions to the Social Network Analysis and Graph Algorithms track at the 30th edition of the Web Conference series (formerly known as WWW), to be held April 19-23, 2021 in Ljubljana, Slovenia (http://www2021.thewebconf.org).
Social networks have radically changed the way people produce and consume online information, further lowering the access barrier and enabling new forms of interaction between people, objects, information, and services. The COVID pandemic proved that such interaction can be of crucial importance when personal contacts need to be reduced and people’s movements are restrained. On the other hand, the vast amount of data available from many online agents and sources has created an unprecedented opportunity to address both new and longstanding questions. At the same time, these systems have also become targets of issues related to fraud, privacy, fairness and transparency. The sheer size of data has also created challenges regarding storage, analysis, compression and sensemaking.
We encourage submissions in all areas of graph theory and algorithms, graph mining, and social network analysis; we also welcome works that integrate ideas from data mining, machine learning, social sciences, algorithmics and computer science theory. This track explicitly focuses on the investigation of graph-based techniques for social networks with the aim of developing new theories, models, and algorithms to make these systems more effective and efficient.
Topics include (but are not limited to):
- Algorithms for Graph Reconstruction, Graph Identification, and Network Inference
- Algorithms for Graph Representation, Sparsification, Sketching, and Compression
- Algorithms for Subgraph and Motif Discovery
- Analysis of Heterogeneous, Signed, Attributed, and Annotated Networks
- Applications of Graph Mining in Neuroscience, Economics, Sociology, etc.
- Deep Learning for Graphs and Networks
- Detecting, Understanding, and Combating Fake News
- Discovering Causal Effects in Networked Environments
- Dynamic Network Analysis and Algorithms for Graph Streams Fairness, Bias, and Transparency of Graph Mining and Learning Algorithms
- Fraud, Spam, and Malice Detection in Relational Domains
- Game Theoretic and Economic Aspects on Graphs and Networks
- Graph Summarization and Visual Analytics
- Influence Propagation and Information Diffusion
- Link Prediction
- Location-aware Social Network Analysis and Mobility
- Mining and Learning in Graphs with Missing Information and Noise
- Multi-relational Graph Analysis
- Network Representation Learning and Graph Embeddings
- Privacy-preserving Graph Algorithms
- Querying and Indexing Algorithms for Massive Graphs
- Social Media Analysis through the Lenses of Networks
- Social Mining, Social Search, and Social Recommendation Systems
- Social Reputation and Trust Management
- Succinct Data Structures for the Manipulation of Static and Dynamic Large Networks and Network-related Data
Submission guidelines, relevant dates, and important policies can be found at http://www2021.thewebconf.org/authors/call-for-papers/.