scholarly journals Accelerating subgraph matching by anchored relationship on labeled graph

2021 ◽  
pp. 107502
Author(s):  
Yunhao Sun ◽  
Wei Jiang ◽  
Shiqi Liu ◽  
Guanyu Li ◽  
Bo Ning
Keyword(s):  
Subgraph Matching ◽  
Labeled Graph

T Wiener Index of Fibonacci Labeled Graph Pn ʘ F4

2018 ◽  
Vol 9 (11) ◽  
pp. 1712-1716
Author(s):  
R. Palanikumar ◽  
A. Rameshkumar
Keyword(s):  
Wiener Index ◽  
Labeled Graph

Reconstructing a Graph from its Neighborhood Lists

1993 ◽  
Vol 2 (2) ◽  
pp. 103-113 ◽  
Author(s):  
Martin Aigner ◽  
Eberhard Triesch
Keyword(s):  
Decision Problem ◽  
Graph Isomorphism ◽  
Bipartite Graphs ◽  
Labeled Graph ◽  
Np Complete

Associate to a finite labeled graph G(V, E) its multiset of neighborhoods (G) = {N(υ): υ ∈ V}. We discuss the question of when a list is realizable by a graph, and to what extent G is determined by (G). The main results are: the decision problem is NP-complete; for bipartite graphs the decision problem is polynomially equivalent to Graph Isomorphism; forests G are determined up to isomorphism by (G); and if G is connected bipartite and (H) = (G), then H is completely described.

LSimRank: Node Similarity in a Labeled Graph

2020 ◽  
pp. 127-144
Author(s):  
Yang Wu ◽  
Ada Wai-Chee Fu ◽  
Cheng Long ◽  
Zitong Chen
Keyword(s):  
Labeled Graph ◽  
Node Similarity

scholarly journals FAST: FPGA-based Subgraph Matching on Massive Graphs

2021 ◽  
Author(s):  
Xin Jin ◽  
Zhengyi Yang ◽  
Xuemin Lin ◽  
Shiyu Yang ◽  
Lu Qin ◽  
...  
Keyword(s):  
Subgraph Matching ◽  
Massive Graphs

scholarly journals Enumerating the Number of Connected Vertices Labeled Graph of Order Six with Maximum Ten Loops and Containing No Parallel Edges

2020 ◽  
Vol 5 (4) ◽  
pp. 131
Author(s):  
Wamiliana Wamiliana ◽  
Amanto Amanto ◽  
Mustofa Usman ◽  
Muslim Ansori ◽  
Fadila Cahya Puri
Keyword(s):  
Connected Graph ◽  
Labeled Graph ◽  
Labeled Graphs

A Graph G (V, E) is said to be a connected graph if for every two vertices on the graph there exist at least a path connecting them, otherwise, the graph is disconnected. Two edges or more that connect the same pair of vertices are called parallel edges, and an edge that starts and ends at the same vertex is called a loop.  A graph is called simple if it containing no loops nor parallel edges. Given n vertices and m edges, m ≥ 1, there are many graphs that can be formed, either connected or disconnected. In this research, we will discuss how to calculate the number of connected vertices labeled graphs of order six (isomorphism graphs are counted as one), with a maximum loop of ten without parallel edges.  

scholarly journals Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

2020 ◽  
Author(s):  
Douglas Meneghetti ◽  
Reinaldo Bianchi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Communication Channels ◽  
Neural Network Architecture ◽  
Graph Representations ◽  
Labeled Graph ◽  
Multiple Agent ◽  
Multi Agent ◽  
Graph Neural Networks

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

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