Big Graph Live

On Smart Query Routing: For Distributed Graph Querying with Decoupled Storage A summary of the USENIX ATC 2018 research paper  by Arijit Khan, Gustavo Segovia, and Donald Kossmann Background: Distributed Graph Querying & Graph Partitioning Graphs with millions of nodes and billions of edges are  ubiquitous to represent highly interconnected structures  including the World Wide Web, social networks, knowledge  graphs, genome and scientific databases, medical and government records. To support online search and  query services (possibly from many clients) with low latency  and high throughput, data centers and cloud operators  consider scale-out solutions, in which the graph and  its data are partitioned horizontally across cheap commodity  servers. We assume that the graph topology and  the data associated with nodes and edges are co-located,  since they are often accessed together. Keeping  with the modern d...

Jointly Finding Seeds and Relevant Tags in Social Networks for Targeted Influence Maximization A summary of the SIGMOD 2018 research paper by Xiangyu KE, Arijit KHAN and Gao CONG [ Influence Maximization ] Nowadays online social networks have become an important medium for campaigners to promote their products. It is quite normal that a social network has millions of users, and it is impossible for a campaigner to directly reach out to all of them via advertisements, free samples, or discounted prices. If we see our friends' tweets praising some products, we probably also adopt them. Therefore, it is important to identify a small set of users (with cardinality  k ) who can influence as many other users as possible in the social network. It is known as the  Influence Maximization problem, and has been widely studied [1, 2].  Moreover, a campaigner can further specify her target customers, either explicitly, or via some constraints (e.g., people in age group 2...