Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Murty, M Narasimha"'
The problem of link prediction is of active interest. The main approach to solving the link prediction problem is based on heuristics such as Common Neighbors (CN) -- more number of common neighbors of a pair of nodes implies a higher chance of them
Externí odkaz:
http://arxiv.org/abs/2111.00271
Usual relations between entities could be captured using graphs; but those of a higher-order -- more so between two different types of entities (which we term "left" and "right") -- calls for a "bipartite hypergraph". For example, given a left set of
Externí odkaz:
http://arxiv.org/abs/2111.00243
Autor:
Thomas, Shyni, Murty, M. Narasimha
Multi Agent Path Finding (MAPF) requires identification of conflict free paths for agents which could be point-sized or with dimensions. In this paper, we propose an approach for MAPF for spatially-extended agents. These find application in real worl
Externí odkaz:
http://arxiv.org/abs/2106.05188
Graph neural networks get significant attention for graph representation and classification in machine learning community. Attention mechanism applied on the neighborhood of a node improves the performance of graph neural networks. Typically, it help
Externí odkaz:
http://arxiv.org/abs/2007.10908
Deep representation learning on non-Euclidean data types, such as graphs, has gained significant attention in recent years. Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vecto
Externí odkaz:
http://arxiv.org/abs/2006.04696
Network representation learning and node classification in graphs got significant attention due to the invent of different types graph neural networks. Graph convolution network (GCN) is a popular semi-supervised technique which aggregates attributes
Externí odkaz:
http://arxiv.org/abs/2002.03392
Centrality is an important notion in complex networks; it could be used to characterize how influential a node or an edge is in the network. It plays an important role in several other network analysis tools including community detection. Even though
Externí odkaz:
http://arxiv.org/abs/1703.07580
Autor:
Gupta, Anubhav, Murty, M. Narasimha
Ranking in bibliographic information networks is a widely studied problem due to its many applications such as advertisement industry, funding, search engines, etc. Most of the existing works on ranking in bibliographic information network are based
Externí odkaz:
http://arxiv.org/abs/1612.08644
Most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a 'flat' form (mega-j
Externí odkaz:
http://arxiv.org/abs/1201.2925