Labeled Graph Sketches

2018 ◽  
Author(s):  
Chunyao Song ◽  
Tingjian Ge
Keyword(s):  
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 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

Graph Applications in Chemoinformatics and Structural Bioinformatics

2011 ◽  
pp. 386-420
Author(s):  
Eleanor Joyce Gardiner
Keyword(s):  
Graph Theory ◽  
Molecular Graph ◽  
Structural Bioinformatics ◽  
Three Dimensions ◽  
Chemical Graph ◽  
Labeled Graph ◽  
3D Space ◽  
Chemical Graph Theory ◽  
Computer Representation ◽  
History Of

The focus of this chapter will be the uses of graph theory in chemoinformatics and in structural bioinformatics. There is a long history of chemical graph theory dating back to the 1860’s and Kekule’s structural theory. It is natural to regard the atoms of a molecule as nodes and the bonds as edges (2D representations) of a labeled graph (a molecular graph). This chapter will concentrate on the algorithms developed to exploit the computer representation of such graphs and their extensions in both two and three dimensions (where an edge represents the distance in 3D space between a pair of atoms), together with the algorithms developed to exploit them. The algorithms will generally be summarized rather than detailed. The methods were later extended to larger macromolecules (such as proteins); these will be covered in less detail.

scholarly journals A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Georgios Alexandridis ◽  
Yorghos Voutos ◽  
Phivos Mylonas ◽  
George Caridakis
Keyword(s):  
Building Blocks ◽  
Sharing Economy ◽  
Urban Landscapes ◽  
Methodological Framework ◽  
Short Term ◽  
Multimodal Data ◽  
Labeled Graph ◽  
Current Sharing ◽  
High Level

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

scholarly journals Dynamic Top-K Interesting Subgraph Query on Large-Scale Labeled Graphs

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 61
Author(s):  
Xiaohuan Shan ◽  
Chunjie Jia ◽  
Linlin Ding ◽  
Xingyan Ding ◽  
Baoyan Song
Keyword(s):  
Biological Networks ◽  
Large Scale ◽  
Maintenance Strategy ◽  
Incremental Maintenance ◽  
Labeled Graph ◽  
Important Means ◽  
Subgraph Query ◽  
Redundant Operation ◽  
The Individual ◽  
Optimization Mechanism

A labeled graph is a special structure with node identification capability, which is often used in information networks, biological networks, and other fields. The subgraph query is widely used as an important means of graph data analysis. As the size of the labeled graph increases and changes dynamically, users tend to focus on the high-match results that are of interest to them, and they want to take advantage of the relationship and number of results to get the results of the query quickly. For this reason, we consider the individual needs of users and propose a dynamic Top-K interesting subgraph query. This method establishes a novel graph topology feature index (GTSF index) including a node topology feature index (NTF index) and an edge feature index (EF index), which can effectively prune and filter the invalid nodes and edges that do not meet the restricted condition. The multi-factor candidate set filtering strategy is proposed based on the GTSF index, which can be further pruned to obtain fewer candidate sets. Then, we propose a dynamic Top-K interesting subgraph query method based on the idea of the sliding window to realize the dynamic modification of the matching results of the subgraph in the dynamic evolution of the label graph, to ensure real-time and accurate results of the query. In addition, considering the factors, such as frequent Input/Output (I/O) and network communication overheads, the optimization mechanism of the graph changes and an incremental maintenance strategy for the index are proposed to reduce the huge cost of redundant operation and global updates. The experimental results show that the proposed method can effectively deal with a dynamic Top-K interesting subgraph query on a large-scale labeled graph, at the same time the optimization mechanism of graph changes and the incremental maintenance strategy of the index can effectively reduce the maintenance overheads.

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