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Sparsity measure of a network graph: Gini index

INFORMATION SCIENCES 2018年 462卷 16-39页

作者： Goswami, Swati Murthy, C. A. Das, Asit K. Indian Stat Inst Machine Intelligence Unit 203 BT Road Kolkata 700108 India Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 Howrah India

This article explores the problem of formulating a general measure of sparsity of network graphs. Based on an available definition sparsity of a dataset, namely Gini index, it provides a way to define sparsity measure of a network graph. We name the sparsity measure so introduced as sparsity index. Sparsity measures are commonly associated with six properties, namely, Robin Hood, Scaling, Rising Tide. Cloning, Bill Gates and Babies. Sparsity index directly satisfies four of these six properties;does not satisfy Cloning and satisfies Scaling for some specific cases. A comparison of the proposed index is drawn with Edge Density (the proportion of the sum of degrees of all nodes in a graph compared to the total possible degrees in the corresponding fully connected graph), by showing mathematically that as the edge density of an undirected graph increases, its sparsity index decreases. The paper highlights how the proposed sparsity measure can reveal important properties of a network graph. Further, a relationship has been drawn analytically between the sparsity index and the exponent term of a power law distribution (a distribution known to approximate the degree distribution of a wide variety of network graphs). To illustrate application of the proposed index, a community detection algorithm for network graphs is presented. The algorithm produces overlapping communities with no input requirement on number or size of the communities;has a computational complexity O(n(2)), where n is the number of nodes of the graph. The results validated on artificial and real networks show its effectiveness. (C) 2018 Elsevier Inc. All rights reserved.

关键词： network graph Sparsity measure Gini index Edge density Degree distribution Power law network community detection algorithm

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Predictions of Drug Metabolism Pathways Through CYP 3A4 Enzyme By Analysing Drug-Target Interactions network graph 13

Predictions of Drug Metabolism Pathways Through CYP 3A4 Enzy...

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IEEE/ACM International Conference on Advances in Social networks Analysis and Mining (ASONAM)

作者： Albrijawi, M. Taleb Ibrahim, Amrou Haj Alhajj, Reda Univ Calgary Dept Comp Sci Calgary AB Canada Istanbul Medipol Univ Dept Comp Engn Istanbul Turkey Univ Southern Denmark Dept Heath Informat Odense Denmark

ISBN: (纸本)9781450391283

The available data of drugs and their targets has increased widely in recent years. Far from the traditional way of studying the drug-target interactions, we propose a network-based computational method to identify new targets for known drugs. In this study, the Stanford Biomedical network Dataset Collection (BIOSNAP Datasets) is used. A network graph is constructed and analyzed to study the relationship between the drugs and their targets. Different centrality and similarity measures analyses are applied and predict new potential metabolism pathways for five drugs, namely (Wortmannin, Voacamine, Vancomycin, Dactinomycin and Arundic acid) through Cytochrome P450 3A4 enzyme in the liver. The application of network theory to the analysis of this dataset reveals a new significant approach. Finally the molecular docking is performed to confirm the results. Also, the importance of the presented method in drug discovery is highlighted/pointed out.

关键词： Drug-target interactions network network graph Metabolism pathways Drug discovery Cytochrome P450 enzyme family CYP3A4

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Constructing a network graph of File Tracking Results Against Information Leakage 17

Constructing a Network Graph of File Tracking Results Agains...

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17th Annual Asia Joint Conference on Information Security (AsiaJCIS)

作者： Yano, Tomohiko Kuzuno, Hiroki Magata, Kenichi SECOM CO LTD Intelligent Syst Lab Mitaka Tokyo Japan Kobe Univ Grad Sch Engn Kobe Hyogo Japan

ISBN: (纸本)9781665473927

It is important for organizations to take measures against information leakage. Confidential files can be leaked through various channels, so it is necessary to have a method to prevent information leakage against various threats. Some of the previous works have utilized the difference of users' legitimate file access patterns, and other works use strings about confidential files, or the similarity of confidential files in the organizations. However, the former works are difficult to detect traitors and unintentional perpetrators, and latter works are difficult to perform when confidential files are significantly transformed through encryption or encoding. Therefore, we need a method for discovering information leakage that are independent of the subjects and of the file transformation formats. In this paper, we present a novel method for file tracking and visualization to assist the discovery of information leakage. In our file tracking method, we track all user processes that read confidential files and files written by these processes. Therefore, tracking is possible whoever manipulate the confidential files and even who even when the data is heavily transformed from the original files. In our visualization method, we present these file tracking results in the form of a network graph. We represent what process the confidential file is read and what file is written by process, by using the flow of a network graph based on the result of confidential file tracking. By using our proposed network graph, it is possible to track events briefly even when the file transforms into another file through multiple events. Additionally, in order to reduce the events needed to focus on as information leakage, we prune the network graph based on past read and write events. By pruning the network graph, visibility is expected to be improved. Our experiment shows that we observed the results of the network graph when files under two information leakage scenarios were moved and copi

关键词： Information leakage network graph Pruning

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Estimating adaptation effort in industry 4.0-enabled systems: Introducing two complexity indices with an evolvable network graph approach

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 40卷

作者： Mabkhot, Mohammed M. Ferreira, Pedro Eaton, William Lohse, Niels Loughborough Univ Intelligent Automat Ctr Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU England

One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness enables coping with new market requirements or introducing new products, as demonstrated by the COVID-19 challenges. However, there are currently no effective methods for measuring the responsiveness or reconfigurability of a system, or for quantifying the effort required to adapt it from one state to another. Adapting a production cell from its current state to a new adapted state requires a significant amount of information about dismantling, reintegrating, and handling physical equipment, as well as updating the software controller. Practitioners often only consider adaptation options for simple process parametrization or at the end of a system's life cycle, overlooking many potential adaptation opportunities. This paper proposes an evolvable network graph approach for supporting reconfiguration decisions by estimating the effort required to adapt the physical structure. Two complexity indexes have been developed to quantify the adaptation activities. An estimation algorithm infers the effort from the difference in the adaptation graphs that represent alternative options. The approach is illustrated in a laboratory-scale cell and applied in two industrial-sized cells, quantifying adaptation times of approximately 58, 7, and 122 h, respectively. This is equivalent to 3129.6 pound, 356.04 pound, and 6118.8 pound, utilizing average hourly rates for system integrators and equipment handlers. The results show that the approach can effectively quantify the adaptation effort for different equipment sizes and connections, estimating the adaptation cost and time from the graph change quickly at around a millisecond and with minimal computational resources.

关键词： Effort estimation Adaptive reconfigurable cell Responsive systems Industry 4.0 System complexity network graph

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Physical Switching Systems: From a network graph to a Hybrid Port Hamiltonian Formulation

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IFAC Proceedings Volumes 2003年第6期36卷 283-288页

作者： M. Magos C. Valentin B. Maschke Laboratoire d'Automatique et de Génie des Procédés LAGEP UMR CNRS 5007 Université Claude Bernard Lyon 1 bat. 308 G ESCPE 43 Bd du 11 Novembre 1918 69622 Villeurbanne cedex France. Tel: (33)4 7243 1866 Fax: (33)4 7243 1899

this paper presents a systematic method, to obtain the hybrid Hamiltonian formulation of a class of linear switched systems (LSS). The advantages of this formulation are that the state of the switching part is explicit and the equations are directly related to the interconnection constraints and dissipation. The resulting port controlled Hamiltonian representation with dissipation is of course hybrid, including continuous and discrete variables. The network graph of the system gives its Dirac structure which is reduced in successive steps to obtain the Hamiltonian formulation.

关键词： hybrid systems non-regular systems linear switched systems modelling dissipation network graph Hamiltonian formulation Dirac structure

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Temporal Statistical Analysis of Degree Distributions in an Undirected Landline Phone Call network graph Series

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DATA 2017年第4期2卷 33-33页

作者： Gjermeni, Orgeta Univ Ismail Qemali Dept Math Str Kosova Vlore 9400 Albania

This article aims to provide new results about the intraday degree sequence distribution considering phone call network graph evolution in time. More specifically, it tackles the following problem. Given a large amount of landline phone call data records, what is the best way to summarize the distinct number of calling partners per client per day? In order to answer this question, a series of undirected phone call network graphs is constructed based on data from a local telecommunication source in Albania. All network graphs of the series are simplified. Further, a longitudinal temporal study is made on this network graphs series related to the degree distributions. Power law and log-normal distribution fittings on the degree sequence are compared on each of the network graphs of the series. The maximum likelihood method is used to estimate the parameters of the distributions, and a Kolmogorov-Smirnov test associated with a p-value is used to define the plausible models. A direct distribution comparison is made through a Vuong test in the case that both distributions are plausible. Another goal was to describe the parameters' distributions' shape. A Shapiro-Wilk test is used to test the normality of the data, and measures of shape are used to define the distributions' shape. Study findings suggested that log-normal distribution models better the intraday degree sequence data of the network graphs. It is not possible to say that the distributions of log-normal parameters are normal.

关键词： longitudinal degree distribution network graph phone call data power law log-normal

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An enhanced graph Analytics Platform (GAP) providing insight in Big network Data

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Journal of Innovation in Digital Ecosystems 2016年第2期3卷 83-97页

作者： Anastasios Drosou Ilias Kalamaras Stavros Papadopoulos Dimitrios Tzovaras Centre for Research & Technology Hellas - Information Technologies Institute Greece Imperial College London - Department of Electrical and Electronic Engineering United Kingdom

Being a widely adapted and acknowledged practice for the representation of inter- and intra-dependent information streams, network graphs are nowadays growing vast in both size and complexity, due to the rapid expansion of sources, types, and amounts of produced data. In this context, the efficient processing of the big amounts of information, also known as Big Data forms a major challenge for both the research community and a wide variety of industrial sectors, involving security, health and financial applications. Serving these emerging needs, the current paper presents a graph Analytics based Platform (GAP) that implements a top-down approach for the facilitation of Data Mining processes through the incorporation of state-of-the-art techniques, like behavioural clustering, interactive visualizations, multi-objective optimization, etc. The applicability of this platform is validated on 2 istinct real-world use cases, which can be considered as characteristic examples of modern Big Data problems, due to the vast amount of information they deal with. In particular, (i) the root cause analysis of a Denial of Service attack in the network of a mobile operator and (ii) the early detection of an emerging event or a hot topic in social media communities. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed platform offers differentiation between different user behaviors that enable the analyst to obtain insight on the network’s operation and to extract the meaningful information in an effortless manner. Dynamic hypothesis formulation techniques exploited by graph traversing and pattern mining, enable the analyst to set concrete network-related hypotheses, and validate or reject them accordingly.

关键词： graph analytics network graph Big data Behavioural clustering Information visualization Hypothesis formulation

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graphDecoder: Recovering Diverse network graphs From Visualization Images via Attention-Aware Learning

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER graphICS 2024年第7期30卷 3074-3088页

作者： Song, Sicheng Li, Chenhui Li, Dong Chen, Juntong Wang, Changbo East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China

DNGs are diverse network graphs with texts and different styles of nodes and edges, including mind maps, modeling graphs, and flowcharts. They are high-level visualizations that are easy for humans to understand but difficult for machines. Inspired by the process of human perception of graphs, we propose a method called graphDecoder to extract data from raster images. Given a raster image, we extract the content based on a neural network. We built a semantic segmentation network based on U-Net. We increase the attention mechanism module, simplify the network model, and design a specific loss function to improve the model's ability to extract graph data. After this semantic segmentation network, we can extract the data of all nodes and edges. We then combine these data to obtain the topological relationship of the entire DNG. We also provide an interactive interface for users to redesign the DNGs. We verify the effectiveness of our method by evaluations and user studies on datasets collected on the internet and generated datasets.

关键词： Attention mechanism chart mining information visualization network graph semantic segmentation

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Vividgraph: Learning to Extract and Redesign network graphs From Visualization Images

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER graphICS 2023年第7期29卷 3169-3181页

作者： Song, Sicheng Li, Chenhui Sun, Yujing Wang, Changbo East China Normal Univ Sch Comp Sci & Technol Shanghai 200050 Peoples R China

network graphs are common visualization charts. They often appear in the form of bitmaps in articles, web pages, magazine prints, and designer sketches. People often want to modify graphs because of their poor design, but it is difficult to obtain their underlying data. In this article, we present Vividgraph, a pipeline for automatically extracting and redesigning graphs from static images. We propose using convolutional neural networks to solve the problem of graph data extraction. Our method is robust to hand-drawn graphs, blurred graph images, and large graph images. We also present a graph classification module to make it effective for directed graphs. We propose two evaluation methods to demonstrate the effectiveness of our approach. It can be used to quickly transform designer sketches, extract underlying data from existing graphs, and interactively redesign poorly designed graphs.

关键词： Data mining Semantics Image color analysis Image segmentation Data visualization Pipelines Image edge detection Information visualization network graph data extraction chart recognition semantic segmentation redesign

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Identifying common components across biological network graphs using a bipartite data model

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BMC Proceedings 2014年第SUPPL 6 PROCEEDINGS OF THE GREAT LAKES BIOINFORMATICS CONFER期8卷 1-6页

作者： Baker, E.J. Culpepper, C. Philips, C. Bubier, J. Langston, M. Chesler, E.J. Department of Computer Science Baylor University Waco TX United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Department of Bioinformatics and Computational Biology Jackson Laboratory Bar Harbor ME United States

The GeneWeaver bipartite data model provides an efficient means to evaluate shared molecular components from sets derived across diverse species, disease states and biological processes. In order to adapt this model for examining related molecular components and biological networks, such as pathway or gene network data, we have developed a means to leverage the bipartite data structure to extract and analyze shared edges. Using the Pathway Commons database we demonstrate the ability to rapidly identify shared connected components among a diverse set of pathways. In addition, we illustrate how results from maximal bipartite discovery can be decomposed into hierarchical relationships, allowing shared pathway components to be mapped through various parent-child relationships to help visualization and discovery of emergent kernel driven relationships. Interrogating common relationships among biological networks and conventional GeneWeaver gene lists will increase functional specificity and reliability of the shared biological components. This approach enables self-organization of biological processes through shared biological networks. © 2014 Baker et al.;licensee BioMed Central Ltd.

关键词： Bipartite graph Biological network network graph Pathway network Shared network

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