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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.


Papers
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Proceedings ArticleDOI
13 Dec 2010
TL;DR: This paper addresses the problem of finding cardinality-constrained connected sub trees in large node-weighted graphs that maximize the sum of weights of selected nodes and provides an efficient constant-factor approximation algorithm for this strongly NP-hard problem.
Abstract: Graphs are increasingly used to model a variety of loosely structured data such as biological or social networks and entity-relationships. Given this profusion of large-scale graph data, efficiently discovering interesting substructures buried within is essential. These substructures are typically used in determining subsequent actions, such as conducting visual analytics by humans or designing expensive biomedical experiments. In such settings, it is often desirable to constrain the size of the discovered results in order to directly control the associated costs. In this paper, we address the problem of finding cardinality-constrained connected sub trees in large node-weighted graphs that maximize the sum of weights of selected nodes. We provide an efficient constant-factor approximation algorithm for this strongly NP-hard problem. Our techniques can be applied in a wide variety of application settings, for example in differential analysis of graphs, a problem that frequently arises in bioinformatics but also has applications on the web.

10 citations

Journal ArticleDOI
01 Sep 2010
TL;DR: The demonstrated system named InZeit (pronounced "insight") assists users by determining insightful time points for a given query, which are the time points at which the top-k time-travel query result changes substantially and for which the user should therefore inspect query results.
Abstract: Web archives are useful resources to find out about the temporal evolution of persons, organizations, products, or other topics. However, even when advanced text search functionality is available, gaining insights into the temporal evolution of a topic can be a tedious task and often requires sifting through many documents.The demonstrated system named InZeit (pronounced "insight") assists users by determining insightful time points for a given query. These are the time points at which the top-k time-travel query result changes substantially and for which the user should therefore inspect query results. InZeit determines the m most insightful time points efficiently using an extended segment tree for in-memory bookkeeping.

10 citations

Journal ArticleDOI
TL;DR: The unique feature of BODHI is that it seamlessly integrates diverse types of data, including taxonomic characteristics, spatial distributions, and genetic sequences, thereby spanning the entire range from molecular to organism-level information.

10 citations

Proceedings ArticleDOI
12 Sep 2018
TL;DR: The Hidden Markov Hawkes Process (HMHP) is proposed that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with user-user and user-topic patterns and finds insightful interactions between topics in real tweets which no existing model is able to do.
Abstract: Social media conversations unfold based on complex interactions between users, topics and time. While recent models have been proposed to capture network strengths between users, users' topical preferences and temporal patterns between posting and response times, interaction patterns between topics has not been studied. We propose the Hidden Markov Hawkes Process (HMHP) that incorporates topical Markov Chains within Hawkes processes to jointly model topical interactions along with user-user and user-topic patterns. We propose a Gibbs sampling algorithm for HMHP that jointly infers the network strengths, diffusion paths, the topics of the posts as well as the topic-topic interactions. We show using experiments on real and semi-synthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines. More interestingly, HMHP finds insightful interactions between topics in real tweets which no existing model is able to do.

10 citations

Proceedings ArticleDOI
24 Oct 2016
TL;DR: Quark-X, an RDF-store and SPARQL processing system for reified RDF data represented in the form of quads, and the results of a comprehensive empirical evaluation of the system over Yago2S and DBpedia datasets are presented.
Abstract: There is a growing trend towards enriching the RDF content from its classical Subject-Predicate-Object triple form to an annotated representation which can model richer relationships such as including fact provenance, fact confidence, higher-order relationships and so on. One of the recommended ways to achieve this is to use reification and represent it as N-Quads "or simply quads" where an additional identifier is associated with the entire RDF statement which can then be used to add further annotations. A typical use of such annotations is to have quantifiable confidence values to be attached to facts. In such settings, it is important to support efficient top-k queries, typically over user-defined ranking functions containing sentence level confidence values in addition to other quantifiable values in the database. In this paper, we present Quark-X, an RDF-store and SPARQL processing system for reified RDF data represented in the form of quads. This paper presents the overall architecture of our system -- illustrating the modifications which need to be made to a native quad store for it to process top-k queries. In Quark-X, we propose indexing and query processing techniques for making top-k querying efficient. In addition, we present the results of a comprehensive empirical evaluation of our system over Yago2S and DBpedia datasets. Our performance study shows that the proposed method achieves one to two order of magnitude speed-up over baseline solutions.

9 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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.

10,141 citations

Journal ArticleDOI
TL;DR: Recent progress about link prediction algorithms is summarized, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods.
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.

2,530 citations

Journal ArticleDOI
TL;DR: YAGO2 as mentioned in this paper is an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space, and it contains 447 million facts about 9.8 million entities.

1,186 citations

Journal ArticleDOI
TL;DR: YAGO is a large ontology with high coverage and precision, based on a clean logical model with a decidable consistency that allows representing n-ary relations in a natural way while maintaining compatibility with RDFS.

912 citations