Srikanta Bedathur

Bio: Srikanta Bedathur is an academic researcher from Indian Institute of Technology Delhi. The author has contributed to research in topics: Computer science & SPARQL. The author has an hindex of 21, co-authored 108 publications receiving 1680 citations. Previous affiliations of Srikanta Bedathur include IBM & Indraprastha Institute of Information Technology.

ARROW: Approximating Reachability Using Random Walks Over Web-Scale Graphs

Abstract: Efficiently answering reachability queries on a directed graph is a fundamental problem and many solutions - theoretical and practical - have been proposed. A common strategy to make reachability query processing efficient, accurate and scalable is to precompute indexes on the graph. However this often becomes impractical, particularly when dealing with large graphs that are highly dynamic or when queries have additional constraints known only at the time of querying. In the former case, indexes become stale very quickly and keeping them up to date at the same speed as changes to the graph is untenable. For the latter setting, currently proposed indexes are often quite bulky and are highly customized to handle only a small class of constraints. In this paper, we propose a first practical attempt to address these issues by abandoning the traditional indexing approach altogether and operating directly on the graph as it evolves. Our approach, called ARROW, uses random walks to efficiently approximate reachability between vertices, building on ideas that have been prevalent in the theory community but ignored by practitioners. Not only is ARROW well suited for highly dynamic settings - as it is index-free, but it can also be easily adapted to handle many different forms of ad-hoc constraints while being competitive with custom-made index structures. In this paper, we show that ARROW, despite its simplicity, is near-accurate and scales to graphs with tens of millions of vertices and hundreds of millions of edges. We present extensive empirical evidence to illustrate these advantages.

Comparing apples and oranges: normalized pagerank for evolving graphs

Abstract: PageRank is the best known technique for link-based importance ranking. The computed importance scores, however, are not directly comparable across different snapshots of an evolving graph. We present an efficiently computable normalization for PageRank scores that makes them comparable across graphs. Furthermore, we show that the normalized PageRank scores are robust to non-local changes in the graph, unlike the standard PageRank measure.

BuzzRank … and the trend is your friend

Abstract: Ranking methods like PageRank assess the importance of Web pages based on the current state of the rapidly evolving Web graph. The dynamics of the resulting importance scores, however, have not been considered yet, although they provide the key to an understanding of the Zeitgeist on the Web. This paper proposes the BuzzRank method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance scores. We experimentally demonstrate the usefulness of BuzzRank on a bibliographic dataset.

Search-Optimized Suffix-Tree Storage for Biological Applications

Abstract: Suffix-trees are popular indexing structures for various sequence processing problems in biological data management. We investigate here the possibility of enhancing the search efficiency of disk-resident suffix-trees through customized layouts of tree-nodes to disk-pages. Specifically, we propose a new layout strategy, called Stellar, that provides significantly improved search performance on a representative set of real genomic sequences. Further, Stellar supports both the standard root-to-leaf lookup queries as well as sophisticated sequencesearch algorithms that exploit the suffix-links of suffix-trees. Our results are encouraging with regard to the ultimate objective of seamlessly integrating sequence processing in database engines.

LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks

Abstract: Location-based social networks (LBSNs) such as Foursquare offer a platform for users to share and be aware of each other’s physical movements. As a result of such a sharing of check-in information with each other, users can be influenced to visit at the locations visited by their friends. Quantifying such influences in these LBSNs is useful in various settings such as location promotion, personalized recommendations, mobility pattern prediction etc. In this paper, we focus on the problem of location promotion and develop a model to quantify the influence specific to a location between a pair of users. Specifically, we develop a joint model called LoCaTe, consisting of (i) user mobility model estimated using kernel density estimates; (ii) a model of the semantics of the location using topic models; and (iii) a model of time-gap between checkins using exponential distribution. We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 – Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that LoCaTe significantly outperforms state-of-the-art models for the same task.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Link prediction in complex networks: A survey

Abstract: Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.