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About 10,000 papers for “Graph”

Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph

Yucong Duan, Lixu Shao, Gongzhu Hu · International Journal of Software Innovation · 2018

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. The authors propose to clarify the expression of knowledge graph as a whole. They clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. The authors also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

Cited by 25

HyperWeighted Graph, SuperHyperWeighted Graph, and MultiWeighted Graph

Takao Fujita · 2025

A weighted graph is a graph in which each edge is assigned a numerical value (weight), typically representing cost, distance, or intensity. In this paper, we revisit and further explore three generalizations of weighted graphs: the Hyperweighted Graph, the Superhyperweighted Graph, and the MultiWeighted Graph. These advanced structures were initially introduced. Our objective is to enhance understanding and broaden awareness of their theoretical foundations and potential applications through renewed analysis and formal refinement.

Cited by 1Open access

Some Meta-Graph Structures: Mixed Graph, DiHyperGraph, Knowledge Graph, Intersection Graph, and Chemical Graph

Takao Fujita · ChemRxiv · 2025

Graph theory studies mathematical structures composed of vertices and edges in order to model relationships and connectivity [1, 2]. A MetaGraph is a higher-level construct in which the vertices are themselves graphs, and the edges represent specified relations among those graphs. An Iterated MetaGraph extends this concept recursively: its vertices are MetaGraphs, producing a hierarchy of graph-of-graphs structures across multiple levels. In this work, we explore the possibility of extending Mixed Graphs, DiHyperGraphs, Knowledge Graphs, Intersection Graphs, and Chemical Graphs within the framework of MetaGraphs and Iterated MetaGraphs.

Cited by 0Open access

Plithogenic Line Graph, Star Graph, and Regular Graph

Takao Fujita · Plithogenic Logic and Computation · 2025

Graph theory, a branch of mathematics, studies relationships between entities using vertices and edges. In this field, Uncertain Graph Theory has emerged to capture the uncertainties inherent in real-world networks. While Turiyam Neutrosophic and Plithogenic Graphs are established within uncertain graph theory, concepts such as Line Graphs, Star Graphs, and Regular Graphs have not been extensively explored in these frameworks. This paper seeks to fill this gap by introducing and developing the concepts of Line Graph, Star Graph, and Regular Graph specifically within the contexts of Turiyam Neutrosophic and Plithogenic Graphs.

Cited by 0Open access

SEP-Graph/sep-graph: SEP-Graph-source-code

Pwrliang · Zenodo (CERN European Organization for Nuclear Research) · 2018

This release includes the source code of the PPoPP'19 paper "SEP-Graph: Finding Shortest Execution Paths for Graph Processing under a Hybrid Framework on GPU ". The artifact evaluation is excluded in this release but is put into the release of SEP-Graph-AE separately. SEP-Graph is an open-source framework for graph processing on GPU. It supports the graph algorithms to be implemented as the combinations of Sync. or Async execution mode, Pull or Push communication method, and Data-driven or Topology-driven vertex traversing mechanism. At runtime, SEP-Graph will try to find the shortest execution path of a graph algorithm in each iteration. This work is designed, implemented, and maintained by the High Performance Computing and Software Lab, led by Professor Xiaodong Zhang from the Department of Computer Science and Engineering, Ohio State University.

Cited by 0Open access

A derived graph: triangle graph of a graph

Sithole, Elias · University of the Witwatersrand, Johannesburg Institutional Repository on DSpace (University of the Witwatersrand, Johannesburg) · 2021

A dissertation submitted in fulfilment of the requirements for the degree of Master of Science to the Faculty of Science, School of Mathematics, University of the Witwatersrand, Johannesburg, 2021

Cited by 0

The mincut graph: intersection graph and graph operator

Kriel, C.W. · University of the Witwatersrand, Johannesburg Institutional Repository on DSpace (University of the Witwatersrand, Johannesburg) · 2021

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy to the Faculty of Science, School of Mathematics, University of the Witwatersrand, Johannesburg, 2021

Cited by 0

Graph-Graph Context Dependency Attention for Graph Edit Distance

Ruiqi Jia, Xianbing Feng, Xiaoqing Lyu et al. · 2023

As a popular similarity measurement, Graph Edit Distance (GED) can be applied to various downstream tasks. Traditional GED solvers have difficulty in generalization and time efficiency, so deep graph similarity learning models have emerged as a promising trend. However, existing methods lack distinguishing embeddings for cross-dependencies between graphs and intra-graph representations. In this paper, we propose a deep network architecture GED-CDA by introducing a graph-graph context dependency attention module to enhance embeddings. Our graph-graph context dependency attention module consists of a cross-attention layer and a self-attention layer, which can both learn the interactions between graph pairs and capture the connections within graphs. In addition, we also introduce a spectral encoding strategy to bring topological information of graphs into node representations. Extensive experimental results on three real-world graph datasets demonstrate the effectiveness of our proposed GED-CDA in the GED task.

Cited by 1

Imbalanced Graph Classification via Graph-of-Graph Neural Networks

Yu Wang, Yuying Zhao, Neil Shah et al. · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow the protocol of balanced data splitting, which misaligns with many real-world scenarios in which some classes have much fewer labels than others. Directly training GNNs under this imbalanced scenario may lead to uninformative representations of graphs in minority classes, and compromise the overall classification performance, which signifies the importance of developing effective GNNs towards handling imbalanced graph classification. Existing methods are either tailored for non-graph structured data or designed specifically for imbalanced node classification while few focus on imbalanced graph classification. To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G2GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs. Globally, we construct a graph of graphs (GoG) based on kernel similarity and perform GoG propagation to aggregate neighboring graph representations. Locally, we employ topological augmentation via masking node features or dropping edges with self-consistency regularization to generate stochastic augmentations of each graph that improve the model generalizability. Extensive graph classification experiments conducted on seven benchmark datasets demonstrate our proposed G2GNN outperforms numerous baselines by roughly 5% in both F1-macro and F1-micro scores.

Cited by 51

Graph Powers and Graph Homomorphisms

Hossein Hajiabolhassan, Ali Taherkhani · ArXiv.org · 2008

In this paper we investigate some basic properties of fractional powers. In this regard, we show that for any rational number $1\leq {2r+1\over 2s+1}< og(G)$, $G^{2r+1\over 2s+1}\longrightarrow H$ if and only if $G\longrightarrow H^{-{2s+1\over 2r+1}}.$ Also, for two rational numbers ${2r+1\over 2s+1} < {2p+1\over 2q+1}$ and a non-bipartite graph $G$, we show that $G^{2r+1\over 2s+1} < G^{2p+1\over 2q+1}$. In the sequel, we introduce an equivalent definition for circular chromatic number of graphs in terms of fractional powers. We also present a sufficient condition for equality of chromatic number and circular chromatic number.

Cited by 0Open access

Attack Graph to Graph Database

Saurav Saraf · IOSR Journal of Computer Engineering · 2014

Databases are an integral part of almost any computing system today, and users heavily rely on the services they provide. When we interact with a computing system, we expect that any data be stored for future use, that the data is able to be looked up quickly, and that we can perform complex queries against the data stored in the database. There are many different emerging database types available for use, such as relational databases, key-value databases, object databases, graph databases, and RDF databases. Each type of database provides a unique set of qualities that have applications in various domains. Our work aims to investigate and compare the performance of relational databases to graph databases in terms of handling Attack Graph data. In this following project work, we transform the Attack graph data (stored in the xml format) to a Graph Database format using Neo4j. This converted format can be viewed using the Neo4j service & local host in the web browser. The further work of the project has also been attached.

Cited by 0Open access