Social media has become a nondetachable part of our life, with the exponential growth of usage in the past decade. Social sites like Twitter, Facebook, Instagram, Flickr, Weibo, etc., with their millions of user base, apart from being a source of entertainment, has proven to be a very useful mean for public opinion generation, news propagation and information broadcasting by authorities. Social media data analysis has been a popular research area for the past few years. Detecting subevents from social media posts to identify an unusual event that requires special attention, especially in a disaster situation, is one of the key researches in this domain. In this article, we have proposed a novel biclustering-based subevent detection method from the Twitter dataset for retrospective analysis of disaster events. First, we have clustered the data matrix using spectral co-clustering. Then we identified subevents (words) and formulated a ranking framework to find the top-ranked subevents within the clusters. Finally, through statistical analysis, we have shown that the proposed framework works better than other existing subevent detection methods.

In complex network analysis, the problem of ranking individual nodes based on their importance has attracted increasing attention from the scientific community due to its vast application, such as identification of influential spreaders for viral marketing or epidemic control, bottlenecks for traffic congestion control, and so on. The growing literature proposes a number of measures to determine the rank order of the network entities where complete information about the nodes and their interaction is available. Degree centrality, PageRank, eigenvector centrality, closeness centrality are few such popular measures. In most real-life scenarios, however, the information about the underlying network is incomplete or affected due to noise. The few works that study the effects of incomplete information on the rank orders show the vulnerability of the rank orders in various topologies. In this paper, we investigate the effects of noise, both random and nonrandom, on the aggregated rank orders determined from the degree, PageRank, eigenvector centrality, and closeness centrality-based rankings. This paper reveals an important insight that even the simple Borda Count ranking has the potential to improve on the accuracy of rank orders in networks with uncertainty. This paper shows the existence of stable nodes in various networks and indicates that the design of the consensus approach based on the properties of the stable nodes can further improve the stability of the rank orders.

Meso-scale structural analysis, like core decomposition has uncovered groups of nodes that play important roles in the underlying complex systems. The existing core decomposition approaches generally focus on node properties like degree and strength. The node centric approaches can only capture a limited information about the local neighborhood topology. In the present work, we propose a group density based core analysis approach that overcome the drawbacks of the node centric approaches. The proposed algorithmic approach focuses on weight density, cohesiveness, and stability of a substructure. The method also assigns an unique score to every node that rank the nodes based on their degree of core-ness. To determine the correctness of the proposed method, we propose a synthetic benchmark with planted core structure. A performance test on the null model is carried out using a weighted lattice without core structures. We further test the stability of the approach against random noise. The experimental results prove the superiority of our algorithm over the state-of-the-arts. We finally analyze the core structures of several popular weighted network models and real life weighted networks. The experimental results reveal important node ranking and hierarchical organization of the complex networks, which give us better insight about the underlying systems.