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Nonparametric method of topic identification using granularity concept and graph-based modeling

NEURAL COMPUTING & APPLICATIONS 2023年第2期35卷 1055-1075页

作者： Ganguli, Isha Sil, Jaya Sengupta, Nandita Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur Howrah India Univ Coll Bahrain Dept Informat Technol Janabiyah Bahrain

This paper aims to classify the large unstructured documents into different topics without involving huge computational resources and a priori knowledge. The concept of granularity is employed here to extract contextual information from the documents by generating granules of words (GoWs), hierarchically. The proposed granularity-based word grouping (GBWG) algorithm in a computationally efficient way group the words at different layers by using co-occurrence measure between the words of different granules. The GBWG algorithm terminates when no new GoW is generated at any layer of the hierarchical structure. Thus multiple GoWs are obtained, each of which contains contextually related words, representing different topics. However, the GoWs may contain common words and creating ambiguity in topic identification. Louvain graph clustering algorithm has been employed to automatically identify the topics, containing unique words by using mutual information as an association measure between the words (nodes) of each GoW. A test document is classified into a particular topic based on the probability of its unique words belong to different topics. The performance of the proposed method has been compared with other unsupervised, semi-supervised, and supervised topic modeling algorithms. Experimentally, it has been shown that the proposed method is comparable or better than the state-of-the-art topic modeling algorithms which further statistically verified with the Wilcoxon Rank-sum Test.

关键词： Granularity Point-wise mutual information graph-based modeling Hierarchical structure Computationally efficient algorithm

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graph-based modeling using association rule mining to detect influential users in social networks

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷 1页

作者： Agouti, Tarik Cadi Ayyad Univ Fac Sci Dept Comp Sci Marrakech Morocco

Information diffusion is an important and attractive field of research in the area of social network analysis, and is at the heart of many studies and applications of knowledge extraction and prediction. Most of these studies have focused on identifying the most influential users and predicting user participation. Nevertheless, despite the extensive research efforts that have been made to tackle these issues, there is still a need for approaches based on association rules mining and graph theory. In this study, we contribute to research in this field by introducing a novel graph-based approach that applies association rules mining to detect influential users. We argue that users influence each other, and that it is possible to predict a user's interests and participation based on previous interactions in the social network. We introduce new concepts and algorithms for more efficient characterization of influential users, and develop an effective approach for the discovery of influencers by using association rule techniques to extract the hidden relationships between users. To evaluate the feasibility and effectiveness of our approach, we propose a new centrality measure called the completeness centrality, and perform an evaluation based on a case study selected from the literature. We then evaluate the effectiveness of the proposed centrality measure by using the susceptible-infected-recovered model and the overlapping similarity measure. The results demonstrate that our measure is feasible and effective for use in identifying influential spreaders, based on a comparison with existing centrality measures such as degree, betweenness, closeness, and eigenvector methods. Finally, to illustrate the efficiency of our approach, experiments were run on 25 generated diffusion graphs, and the results showed that our approach could achieve a high level of performance in terms of computational time for large-scale networks.

关键词： Social networks Influence maximization Influence spreading Association rule mining graph-based modeling

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Non-parametric Method of Topic Identification Using Granularity Concept and graph-based modeling 6

Non-parametric Method of Topic Identification Using Granular...

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6th International Conference on Soft Computing and Machine Intelligence (ISCMI)

作者： Ganguli, Isha Sil, Jaya Sengupta, Nandita Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur Howrah India Univ Coll Bahrain Dept Informat Technol Bahrain Bahrain

ISBN: (纸本)9781728145778

This paper aims to classify the large unstructured documents into different topics without involving huge computational power and a priori knowledge. The concept of granularity is employed here for extracting contextual information from the documents by generating granules of words (GoWs), hierarchically. In a novel computationally efficient w ay, the proposed Granularity based Word Grouping (GBWG) algorithm exploits the co-occurrence between the words of different granules to group the contextually related words, representing different topics. However, the topics are coarsely generated, containing common words. A graph-based method has been proposed to precisely identify the topics using the Louvain graph clustering algorithm. The algorithm clusters the words using mutual information as an association measure between the words of each granule. Each cluster containing unique words, represents different topics. Finally, a test document is classified into a particular topic, corresponding to the cluster having the maximum probability of belongings of the words in the test sequence. The Performance of the proposed method has been compared with other unsupervised, semi-supervised and supervised topic modeling algorithms and we demonstrate that the result is comparable or better than the state of the art algorithms.

关键词： granularity pointwise mutual information (PMI) graph-based modeling hierarchical structure

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modeling of an aircraft structural health monitoring sensor network for operational impact assessment

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第1期21卷 208-224页

作者： Buechter, Kai-Daniel Saez, Carlos Sebastia Steinweg, Dominik Bauhaus Luftfahrt eV Taufkirchen Germany Tech Univ Munich Inst Aircraft Design Garching Germany

Structural health monitoring (SHM) of aircraft components can improve maintenance operations, potentially reducing costs for inspections, unscheduled maintenance events, and unexpected delays. On the other hand, aircraft safety and net present value can be adversely influenced by false alarms, missed detections, system costs, and weight and power requirements of the SHM system. In order to gain a better understanding into the latter, we present a weight and power model for a sensor network, comprising sensors, interrogators, data collectors, and wiring. We assess the net benefit of using SHM in terms of reduced expenditure as function of network coverage, considering a corresponding potential in reducing the inspection effort.

关键词： Structural health monitoring sensor networks aircraft maintenance MRO prognostics and health management aerospace structures graph-based modeling

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On the Conceptualization of Tools for Traceability-based Change Impact Analysis using a Domain-specific modeling Approach

On the Conceptualization of Tools for Traceability-based Cha...

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ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS)

作者： Awadid, Afef Boyer, Remi Technol Res Inst Systx Dept Res & Technol Palaiseau France

ISBN: (纸本)9798350324983

In the context of system architecture design, design changes are deemed as an important issue in the field of cyber-physical systems. This is due to their costs that can increase up to several folds with each step of the life cycle, from conceptual design to operation. Accordingly, before putting such changes into practice, their impacts need to be analyzed based on traceability of dependencies among elements in system artifacts. However, traceability-based Change Impact Analysis (CIA) is time consuming and error-prone process, if performed manually. Thus, a number of tools have been developed in this regard. Yet, in the absence of approaches guiding the specification and implementation of such tools, research reveals their inadequacy for the needs of the industry. To overcome this, this paper proposes a domain-specific modeling approach for the conceptualization of traceability-based CIA tools. Such approach is composed of three building blocks. First, a language for traceability-based CIA designed around the concept of "CIA parameters" (i.e., all kind of information that could contribute to quantitative or qualitative change impact assessment). Second, a modeling procedure referring to a multi-view modeling approach that explains how to use the traceability language to allow creation and machine processing of models (i.e., analysis of models in terms of change impact). Third, mechanisms and algorithms focusing on the implementation of the modeling procedure on a metamodeling platform. The proposed approach is thus an attempt to bridge the gap between design and implementation of traceability-based CIA tools. The approach is illustrated on an example from the aviation field.

关键词： design changes traceability-based change impact analysis CIA parameters domain-specific modeling multi-view modeling graph-based modeling

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A graph-based approach for the structural analysis of road and building layouts

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Geo-Spatial Information Science 2019年第1期22卷 59-72页

作者： Mathieu Domingo Rémy Thibaud Christophe Claramunt Department of Computer Science University of Pau and Pays de l’AdourPauFrance Naval Academy Research Institute Brest CedexFrance

A better understanding of the relationship between the structure and functions of urban and suburban spaces is one of the avenues of research still open for geographical information *** research presented in this paper develops several graph-based metrics whose objective is to characterize some local and global structural properties that reflect the way the overall building layout can be cross-related to the one of the road *** structural properties are modeled as an aggregation of parcels,buildings,and road *** introduce several computational measures(Ratio Minimum Distance,Minimum Ratio Minimum Distance,and Metric Compactness)that respectively evaluate the capability for a given road to be connected with the whole road *** measures reveal emerging sub-network structures and point out differences between less-connective and moreconnective parts of the *** on these local and global properties derived from the topological and graph-based representation,and on building density metrics,this paper proposes an analysis of road and building layouts at different levels of *** metrics developed are applied to a case study in which the derived properties reveal coherent as well as incoherent neighborhoods that illustrate the potential of the approach and the way buildings and roads can be relatively connected in a given urban ***,and by integrating the parcels and buildings layouts,this approach complements other previous and related works that mainly retain the configurational structure of the urban network as well as morphological studies whose focus is generally limited to the analysis of the building layout.

关键词： Urban and suburban spaces graph-based modeling structural analysis GIS

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graph-based Topic-Focused Retrieval in Distributed Camera Network

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IEEE TRANSACTIONS ON MULTIMEDIA 2013年第8期15卷 2046-2057页

作者： Xu, Jiejun Jagadeesh, Vignesh Ni, Zefeng Sunderrajan, Santhoshkumar Manjunath, B. S. Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Calif Santa Barbara Santa Barbara CA 93106 USA

Wide-area wireless camera networks are being increasingly deployed in many urban scenarios. The large amount of data generated from these cameras pose significant information processing challenges. In this work, we focus on representation, search and retrieval of moving objects in the scene, with emphasis on local camera node video analysis. We develop a graph model that captures the relationships among objects without the need to identify global trajectories. Specifically, two types of edges are defined in the graph: object edges linking the same object across the whole network and context edges linking different objects within a spatial-temporal proximity. We propose a manifold ranking method with a greedy diversification step to order the relevant items based on similarity as well as diversity within the database. Detailed experimental results using video data from a 10-camera network covering bike paths are presented.

关键词： Distributed camera network diverse and relevant ranking graph-based modeling information search and retrieval

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An approach to model network exploitations using exploitation graphs

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR modeling AND SIMULATION INTERNATIONAL 2006年第8期82卷 523-541页

作者： Li, We Vaughn, Rayford B. Dandass, Yoginder S. Nova SE Univ Grad Sch Comp & Informat Sci Ft Lauderdale FL 33314 USA Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA

In this article, a modeling process is defined to address challenges in analyzing attack scenarios and mitigating vulnerabilities in networked environments. Known system vulnerability data, system configuration data, and vulnerability scanner results are considered to create exploitation graphs (e-graphs) that are used to represent attack scenarios. Experiments carried out in a cluster computing environment showed the usefulness of proposed techniques in providing in-depth attack scenario analyses for security engineering. Critical vulnerabilities can be identified by employing graph algorithms. Several factors were used to measure the difficulty in executing an attack. A cost/benefit analysis was used for more accurate quantitative analysis of attack scenarios. The authors also show how the attack scenario analyses better help deployment of security products and design of network topologies.

关键词： exploitation graph (e-graph) vulnerability graph graph-based modeling computer security

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Building occupancy simulation and data assimilation using a graph-based agent-oriented model

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2018年 502卷 270-287页

作者： Rai, Sanish Hu, Xiaolin West Virginia Univ Inst Technol 410 Neville St Beckley WV 25801 USA Georgia State Univ 25 Pk Pl Ne Atlanta GA 30303 USA

Building occupancy simulation and estimation simulates the dynamics of occupants and estimates their real-time spatial distribution in a building. It requires a simulation model and an algorithm for data assimilation that assimilates real-time sensor data into the simulation model. Existing building occupancy simulation models include agent-based models and graph-based models. The agent-based models suffer high computation cost for simulating large numbers of occupants, and graph-based models overlook the heterogeneity and detailed behaviors of individuals. Recognizing the limitations of existing models, this paper presents a new graph-based agent-oriented model which can efficiently simulate large numbers of occupants in various kinds of building structures. To support real-time occupancy dynamics estimation, a data assimilation framework based on Sequential Monte Carlo Methods is also developed and applied to the graph-based agent-oriented model to assimilate real-time sensor data. Experimental results show the effectiveness of the developed model and the data assimilation framework. The major contributions of this work are to provide an efficient model for building occupancy simulation that can accommodate large numbers of occupants and an effective data assimilation framework that can provide real-time estimations of building occupancy from sensor data. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Building occupancy simulation Data assimilation Agent-based simulation graph-based modeling Sequential Monte Carlo methods

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Multiview Skeletal Interaction Recognition Using Active Joint Interaction graph

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IEEE TRANSACTIONS ON MULTIMEDIA 2016年第11期18卷 2293-2302页

作者： Li, Meng Leung, Howard City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

This paper addresses the problem of recognizing human skeletal interactions using multiview data captured from depth sensors. The interactions among people are important cues for group and crowd human behavior analysis. In this paper, we focus on modeling the person-person skeletal interactions for human activity recognition. First, we propose a novel graph model in each single-view case to encode class-specific person-person interaction patterns. Particularly, we model each person-person interaction by an attributed graph, which is designed to preserve the complex spatial structure among skeletal joints according to their activity levels as well as the spatio-temporal joint features. Then, combining the graph models for each single-view case, we propose the multigraph model to characterize each multiview interaction. Finally, we apply a general multiple kernel learning method to determine the optimal kernel weights for the proposed multigraph model while the optimal classifier is jointly learned. We evaluate the proposed approach on the M-2 I dataset, the SBU Kinect interaction dataset, and our interaction dataset. The experimental results show that our proposed approach outperforms several existing interaction recognition methods.

关键词： Activity analysis depth sensor graph-based modeling graph kernel human interaction recognition multiview multiple kernel learning

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