Results 11 to 20 of about 6,572,416 (223)
Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2022 Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing.
Jhony H. Giraldo +4 more
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Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation [PDF]
AAAI Conference on Artificial Intelligence, 2021 Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services.
Chao Huang +8 more
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A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks [PDF]
International Conference on Learning Representations, 2022 Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs). While previous works have only demonstrated that oversmoothing is inevitable when the number of graph convolutions tends to infinity, in this paper, we precisely
X. Wu +3 more
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HARMONIC CENTRALIZATION OF SOME GRAPH FAMILIES
Advances and Applications in Discrete Mathematics, 2022 21 pages, 5 figures.
Ortega, Jose Mari E., Eballe, Rolito G.
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On harmonious chromatic number of triple star graph [PDF]
Journal of Hyperstructures, 2016 A Harmonious coloring of a graph G is a proper vertex coloring of G, in which every pair of colors appears on at most one pair of adjacent vertices and the harmonious chromatic number of graph G is the minimum number of colors needed for the harmonious ...
Akhlak Mansuri
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On the r-dynamic coloring of some fan graph families
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, 2021 In this paper, we determine the r-dynamic chromatic number of the fan graph Fm,n and determine sharp bounds of this graph invariant for four related families of graphs: The middle graph M(Fm,n), the total graph T (Fm,n), the central graph C(Fm,n) and the
Falcón Raúl M. +3 more
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Central Clustering of Attributed Graphs [PDF]
Machine Learning, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jain, Brijnesh J., Wysotzki, Fritz
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The Maven Dependency Graph: A Temporal Graph-Based Representation of Maven Central [PDF]
IEEE Working Conference on Mining Software Repositories, 2019 The Maven Central Repository provides an extraordinary source of data to understand complex architecture and evolution phenomena among Java applications. As of September 6, 2018, this repository includes 2.8M artifacts (compiled piece of code implemented
Amine Benelallam +4 more
semanticscholar +1 more source
Adaptive Propagation Graph Convolutional Networks Based on Attention Mechanism
Information, 2022 The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central node ...
Chenfang Zhang, Yong Gan, Ruisen Yang
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Graph neural networks in particle physics [PDF]
Machine Learning: Science and Technology, 2020 Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs—sets of elements and their pairwise relations—and are a central method within ...
Jonathan Shlomi +2 more
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