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

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.

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.

scholarly journals Finite simple labeled graph C⁎-algebras of Cantor minimal subshifts

2017 ◽  
Vol 446 (1) ◽  
pp. 395-410 ◽  
Author(s):  
Ja A Jeong ◽  
Eun Ji Kang ◽  
Sun Ho Kim ◽  
Gi Hyun Park
Keyword(s):  
C Algebras ◽  
Labeled Graph

Link prediction via layer relevance of multiplex networks

2017 ◽  
Vol 28 (08) ◽  
pp. 1750101 ◽  
Author(s):  
Yabing Yao ◽  
Ruisheng Zhang ◽  
Fan Yang ◽  
Yongna Yuan ◽  
Qingshuang Sun ◽  
...  
Keyword(s):  
Structural Properties ◽  
Link Prediction ◽  
Structural Information ◽  
Similarity Index ◽  
Single Layer ◽  
Structural Features ◽  
Prediction Performance ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Node Similarity

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

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