Zobrazeno 1 - 10
of 177
pro vyhledávání: '"Bera, Suman"'
We study the problem of approximately counting cliques and near cliques in a graph, where the access to the graph is only available through crawling its vertices; thus typically seeing only a small portion of it. This model, known as the random walk
Externí odkaz:
http://arxiv.org/abs/2212.03957
A fundamental problem in mathematics and network analysis is to find conditions under which a graph can be partitioned into smaller pieces. The most important tool for this partitioning is the Fiedler vector or discrete Cheeger inequality. These resu
Externí odkaz:
http://arxiv.org/abs/2211.06352
Autor:
Singh, Yeshwant, Biswas, Anupam, Bora, Angshuman, Malakar, Debashish, Chakraborty, Subham, Bera, Suman
In recent years, multi-task learning has turned out to be of great success in various applications. Though single model training has promised great results throughout these years, it ignores valuable information that might help us estimate a metric b
Externí odkaz:
http://arxiv.org/abs/2209.13444
Computing a dense subgraph is a fundamental problem in graph mining, with a diverse set of applications ranging from electronic commerce to community detection in social networks. In many of these applications, the underlying context is better modell
Externí odkaz:
http://arxiv.org/abs/2204.08106
Counting homomorphisms of a constant sized pattern graph $H$ in an input graph $G$ is a fundamental computational problem. There is a rich history of studying the complexity of this problem, under various constraints on the input $G$ and the pattern
Externí odkaz:
http://arxiv.org/abs/2010.08083
We consider the problem of counting the number of copies of a fixed graph $H$ within an input graph $G$. This is one of the most well-studied algorithmic graph problems, with many theoretical and practical applications. We focus on solving this probl
Externí odkaz:
http://arxiv.org/abs/2010.05998
In this paper, we initiate the study of fair clustering that ensures distributional similarity among similar individuals. In response to improving fairness in machine learning, recent papers have investigated fairness in clustering algorithms and hav
Externí odkaz:
http://arxiv.org/abs/2006.12589
Autor:
Bera, Suman K., Seshadhri, C.
Triangle counting is a fundamental problem in the analysis of large graphs. There is a rich body of work on this problem, in varying streaming and distributed models, yet all these algorithms require reading the whole input graph. In many scenarios,
Externí odkaz:
http://arxiv.org/abs/2006.11947
'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing
Externí odkaz:
http://arxiv.org/abs/2005.08642
Autor:
Bera, Suman K., Seshadhri, C.
We revisit the well-studied problem of triangle count estimation in graph streams. Given a graph represented as a stream of $m$ edges, our aim is to compute a $(1\pm\varepsilon)$-approximation to the triangle count $T$, using a small space algorithm.
Externí odkaz:
http://arxiv.org/abs/2003.13151