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, 2022 Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collection by specifying queries that describe their information needs. Traditionally, the data elements
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Embedding graphs on Grassmann manifold
Neural Networks, 2022 Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction. Given the non-Euclidean structural property of graphs, preserving the original graph data's similarity relationship in the embedded space needs specific tools and a similarity metric.
Bingxin Zhou +4 more
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Regular embeddings of a graph [PDF]
Pacific Journal of Mathematics, 1983 In this paper we study embeddings of a graph G in Euclidean space R" that are 'regular' in the following sense: given any two distinct vertices u and v of G, the distance between the corresponding points in R" equals a if u and v are adjacent, and equals β otherwise.
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Adaptive Partitioning for Large-Scale Dynamic Graphs [PDF]
, 2013 —In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce
Martella, Claudio +13 more
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Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
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Improved Skip-Gram Based on Graph Structure Information
Sensors, 2023 Applying the Skip-gram to graph representation learning has become a widely researched topic in recent years. Prior works usually focus on the migration application of the Skip-gram model, while Skip-gram in graph representation learning, initially ...
Xiaojie Wang, Haijun Zhao, Huayue Chen
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Tits alternatives for graph products
, 2015 We discuss various types of Tits Alternative for subgroups of graph products of groups, and prove that, under some natural conditions, a graph product of groups satisfies a given form of Tits Alternative if and only if each vertex group satisfies this ...
Antolin, Yago, Minasyan, Ashot
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FEBS Letters, EarlyView.Obesity raises blood levels of PAI‐1, a protein linked to metabolic dysfunction‐associated steatotic liver disease in people with obesity. In female mice fed a high‐fat diet, partially lowering PAI‐1 led to smaller subcutaneous fat cells and lower liver cholesterol, without changing body weight or insulin sensitivity.
Claudia E. Ramirez Bustamante +10 more
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InformaticsThe combination of Bidirecional Encoder Representations from Transformers (BERT) and Graph Neural Networks (GNNs) has been extensively explored in the text classification literature, usually employing BERT as a feature extractor combined with ...
Eliton Luiz Scardin Perin +3 more
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CoRR, 2019 Representation learning has recently been successfully used to create vector representations of entities in language learning, recommender systems and in similarity learning. Graph embeddings exploit the locality structure of a graph and generate embeddings for nodes which could be words in a language, products of a retail website; and the nodes are ...
Shalin Shah, Venkataramana B. Kini
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