Big Graph Live

Reliability Maximization in Uncertain Graphs A summary of the IEEE TKDE 2020 research  paper  by  Xiangyu Ke, Arijit Khan, Mohammad Al Hasan, and Rojin Rezvansangsari [Background]    Rich expressiveness of probabilistic graphs and their  utility to model the inherent uncertainty in a wide  range of applications have prompted a large number of  research works on probabilistic graphs by the data management  research communities [1].  In an uncertain graph setting, Network Reliability is a  well-studied problem [2], [3], which requires to measure the  probability that a target node is reachable from a source  node. Reliability has been widely studied in device networks,  i.e., networks whose nodes are electronic devices  and the (physical) links between such devices have a probability  of failure [4]. More recently, the attention has been  shifted to social, communication, transportation, genomic...

Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs A summary of the IEEE ICDE 2020 research  paper  by Yuxiang Wang, Arijit Khan, Tianxing Wu, Jiahui Jin, and Haijiang Yan. Background: Knowledge graphs (such as DBpedia [1], Yago [2], and Freebase [3]) have been constructed in recent years, managing large-scale and real-world facts as a graph [4]. In such graphs, each node represents an entity with attributes, and each edge denotes a relationship between two entities.  Answering Graph Query on Knowledge Graphs: Querying knowledge graphs is essential for a wide range of applications, e.g., question answering and semantic search [5]. For example, consider that a user wants to find all cars produced in Germany . One can come up with a reasonable graph representation of this query as a query graph G Q , and identify the exact or approximate matches of G Q in a knowledge graph G using graph query models [6]–[10]. Correct a...

Measurements, Analyses, and Insights on the Entire Ethereum Blockchain Network A summary of the WebConf 2020 (formerly WWW) research  paper  by Xi Tong Lee, Arijit Khan, Sourav Sen Gupta, Yu Hann Ong, and Xuan Liu. Background of the Ethereum Blockchain :  It has been more than ten years since Bitcoin [1] introduced the era of decentralized community-controlled currency. Since then, several cryptocurrency variants like Litecoin, Namecoin, Dash, Zcash, have been introduced. Blockchains are increasingly becoming popular due to the prevalence of such cryptocurrencies and decentralized applications. Decentralized applications are written on the framework of numerous blockchain networks like Hyperledger, Corda, Ripple, Stellar, EOS, NEO, IOTA, and many more. Among them, Ethereum [2] is a distributed public blockchain network that focuses on running code (smart contracts) for decentralized applications. More simply, it is a platform for sharing information in a gl...

Maximizing Contrasting Opinions in Signed Social Networks A summary of the IEEE BigData 2019 research  paper  by Kaivalya Rawal and Arijit Khan. Background:  A central characteristic of social networks is that it facilitates rapid dissemination of information among large groups of individuals [1]. Online social networks, such as Facebook, Twitter, LinkedIn, Flickr, and Digg are used for spreading ideas and messages. Users’ behaviors and opinions are highly affected by their friends in social networks, which is defined as the social influence. Motivated by various real-world applications, e.g., viral marketing [2], social and political campaigning [3], social influence studies have attracted extensive research attention. The classic influence maximization problem [4], [2] identifies the top-k seed users in a social network such that the expected number of influenced users in the network, starting from those seeds and following an influence diffusion model,...