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Thematische Bibliographien / Behavioral graph / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Behavioral graph“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

RAFE, VAHID, and ADEL T. RAHMANI. "A NOVEL APPROACH TO VERIFY GRAPH SCHEMA-BASED SOFTWARE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 06 (2009): 857–70. http://dx.doi.org/10.1142/s0218194009004398.

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Graph Grammars have recently become more and more popular as a general formal modeling language. Behavioral modeling of dynamic systems and model to model transformations are a few well-known examples in which graphs have proven their usefulness in software engineering. A special type of graph transformation systems is layered graphs. Layered graphs are a suitable formalism for modeling hierarchical systems. However, most of the research so far concentrated on graph transformation systems as a modeling means, without considering the need for suitable analysis tools. In this paper we concentrat

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Ye, Lin, Xiaohui Chen, Haiyan Liu, et al. "Vessel Type Recognition Using a Multi-Graph Fusion Method Integrating Vessel Trajectory Sequence and Dependency Relations." Journal of Marine Science and Engineering 12, no. 12 (2024): 2315. https://doi.org/10.3390/jmse12122315.

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In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks the crucial significance of the spatial dependency relationships among trajectory points, posing challenges for comprehensively capturing the intricate features of vessel travel patterns. To address this limitation, our study introduces a novel multi-graph fusion representation method that integrates both trajectory sequences and dependency relations

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DU, XUEHONG, JIANXIN JIAO, and MITCHELL M. TSENG. "Product family modeling and design support: An approach based on graph rewriting systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 2 (2002): 103–20. http://dx.doi.org/10.1017/s0890060402020097.

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Earlier research on product family design (PFD) often highlights isolated and successful empirical studies with a limited attempt to explore the modeling and design support issues surrounding this economically important class of engineering design problems. This paper proposes a graph rewriting system to organize product family data according to the underpinning logic and to model product derivation mechanisms for PFD. It represents the structural and behavioral aspects of product families as family graphs and related graph operations, respectively. The derivation of product variants becomes a

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Wang, Yan, Zhixuan Chu, Xin Ouyang, et al. "LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (2024): 19189–96. http://dx.doi.org/10.1609/aaai.v38i17.29887.

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Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self

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Paul, August Winter, Mbonga Mayala Roger, and Namayanja Proscovia. "The Eigen-chromatic Ratio of Classes of Graphs: Asymptotes, Areas and Molecular Stability." Journal of Progressive Research in Mathematics 12, no. 2 (2017): 1834–52. https://doi.org/10.5281/zenodo.3975878.

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two graph theoretical concepts of energy and chromatic number. The energy of a graph, the sum of the absolute values of the eigenvalues of the adjacency matrix of a graph, arose historically as a result of the energy of the benzene ring being identical to that of the sum of the absolute values of the eigenvalues of the adjacency matrix of the cycle graph on n vertices (see [18]). The chromatic number of a graph is the smallest number of colour classes that we can partition the vertices of a graph such that each edge of the graph has ends that do not belong to the same colour class, and applica

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Wang, Yingxu, and Aderemi Adewumi. "The Formal Design Models of Digraph Architectures and Behaviors." International Journal of Software Science and Computational Intelligence 4, no. 1 (2012): 100–129. http://dx.doi.org/10.4018/jssci.2012010105.

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Graphs are one of the most fundamental and widely used non-linear hierarchical structures of linked nodes. Problems in sciences and engineering can be formulated and solved by the graph model. This paper develops a comprehensive design pattern of formal digraphs using the Doubly-Linked List (DLL) architecture. The most complicated form of graphs known as the weighted digraph is selected as a general graph model, based on it simple graphs such as nondirected and/or nonweighted ones can be easily derived and tailored. A rigorous denotational mathematics, Real-Time Process Algebra (RTPA), is adop

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Heckel, Reiko, and Sebastian Thöne. "Behavioral Refinement of Graph Transformation-Based Models." Electronic Notes in Theoretical Computer Science 127, no. 3 (2005): 101–11. http://dx.doi.org/10.1016/j.entcs.2004.08.037.

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Ma, Xulin, Jiajia Tan, Linan Zhu, Xiaoran Yan, and Xiangjie Kong. "GSRec: A Graph-Sequence Recommendation System Based on Reverse-Order Graph and User Embedding." Mathematics 12, no. 1 (2024): 164. http://dx.doi.org/10.3390/math12010164.

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At present, sequence-based models have various applications in recommendation systems; these models recommend the interested items of the user according to the user’s behavioral sequence. However, sequence-based models have a limitation of length. When the length of the user’s behavioral sequence exceeds the limitation of the model, the model cannot take advantage of the complete behavioral sequence of the user and cannot know the user’s holistic interests. The accuracy of the model then goes down. Meanwhile, sequence-based models only pay attention to the sequential signals of the data but do

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Garnier, Elodie M., Nastasia Fouret, and Médéric Descoins. "ViSiElse: an innovative R-package to visualize raw behavioral data over time." PeerJ 8 (February 3, 2020): e8341. http://dx.doi.org/10.7717/peerj.8341.

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The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral

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Xiao, Fei, Yi Sun, Donggao Du, Xuelei Li, and Min Luo. "A Novel Malware Classification Method Based on Crucial Behavior." Mathematical Problems in Engineering 2020 (March 21, 2020): 1–12. http://dx.doi.org/10.1155/2020/6804290.

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Recently, some graph-based methods have been proposed for malware detection. However, current malware is generally characterized by sophisticated behaviors, which makes graph-based malware detection extremely challenging. To address this issue, we propose a graph repartition algorithm by transforming API call graphs into fragment behaviors based on programs’ dynamic execution traces. The proposed algorithm relies on the N-order subgraph (NSG) for constructing the appropriate fragment behavior. Moreover, we improve the term frequency-inverse document frequency- (TF-IDF-) like measure and inform

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Zhang, Suqi, Xinxin Wang, Rui Wang, Junhua Gu, and Jianxin Li. "Knowledge Graph Recommendation Model Based on Feature Space Fusion." Applied Sciences 12, no. 17 (2022): 8764. http://dx.doi.org/10.3390/app12178764.

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The existing recommendation model based on a knowledge graph simply integrates the behavior features in a user–item bipartite graph and the content features in a knowledge graph. However, the difference between the two feature spaces is ignored. To solve this problem, this paper presents a new recommendation model named the knowledge graph recommendation model based on feature space fusion (KGRFSF). Specifically, in the behavioral feature space, the behavioral features of users and items are constructed by extracting the behavioral feature from the user–item bipartite graph. In the content fea

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Li, Jianfeng, and Dexiang Yang. "Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs." Systems 11, no. 11 (2023): 539. http://dx.doi.org/10.3390/systems11110539.

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The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bi

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Xu, Qingling, Dawei Zhao, Shumian Yang, Lijuan Xu, and Xin Li. "Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks." Electronics 12, no. 23 (2023): 4817. http://dx.doi.org/10.3390/electronics12234817.

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Android malware detection is a critical research field due to the increasing prevalence of mobile devices and apps. Improved methods are necessary to address Android apps’ complexity and malware’s elusive nature. We propose an approach for Android malware detection based on Graph Convolutional Networks (GCNs). Our method focuses on learning the behavioral-level features of Android applications using the call graph extracted from the application’s Dex file. Combining the call graph with sensitive permissions and opcodes creates a new subgraph representing the application’s runtime behavior. Sub

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Wang, Xiaodong, Pei He, Hongjing Yao, Xiangnan Shi, Jiwei Wang, and Yangming Guo. "A Dual Fusion Pipeline to Discover Tactical Knowledge Guided by Implicit Graph Representation Learning." Mathematics 12, no. 4 (2024): 528. http://dx.doi.org/10.3390/math12040528.

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Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. However, traditional methods are limited in capturing sequential and global representation due to their reliance on prior knowledge or feature engineering. The current models based on deep learning focus on extracting implicit behavioral characteristics from combat process data, overlooking the embedded martial knowledge within the recognition of comb

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Dworkin, Lili, and Michael Kearns. "From "In" to "Over": Behavioral Experiments on Whole-Network Computation." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 52–61. http://dx.doi.org/10.1609/hcomp.v3i1.13223.

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We report on a series of behavioral experiments in human computation on three different tasks over networks: graph coloring, community detection (or graph clustering), and competitive contagion. While these tasks share similar action spaces and interfaces, they capture a diversity of computational challenges: graph coloring is a search problem, clustering is an optimization problem, and competitive contagion is a game-theoretic problem. In contrast with most of the prior literature on human-subject experiments in networks, in which collectives of subjects are embedded "in" the network, and hav

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Oliveira, Angelo Schranko de, and Renato Jos´e Sassi. "Behavioral Malware Detection using Deep Graph Convolutional Neural Networks." International Journal of Computer Applications 174, no. 29 (2021): 1–8. http://dx.doi.org/10.5120/ijca2021921218.

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Wang, Junjie, Keith W. Hipel, Liping Fang, Haiyan Xu, and D. Marc Kilgour. "Behavioral Analysis in the Graph Model for Conflict Resolution." IEEE Transactions on Systems, Man, and Cybernetics: Systems 49, no. 5 (2019): 904–16. http://dx.doi.org/10.1109/tsmc.2017.2689004.

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Kong, Jun, Kang Zhang, Jing Dong, and Dianxiang Xu. "Specifying behavioral semantics of UML diagrams through graph transformations." Journal of Systems and Software 82, no. 2 (2009): 292–306. http://dx.doi.org/10.1016/j.jss.2008.06.030.

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Shen, Lijuan, and Liping Jiang. "Eliminating bias: enhancing children’s book recommendation using a hybrid model of graph convolutional networks and neural matrix factorization." PeerJ Computer Science 10 (February 29, 2024): e1858. http://dx.doi.org/10.7717/peerj-cs.1858.

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Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users’ book ratings serving as the weights of the edge

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Yao, Yanhua. "A novel approach towards utilizing graph analyzing objects arrangement – case studies from Airbnb homes in New York and Boston." Proceedings of the Design Society 4 (May 2024): 175–84. http://dx.doi.org/10.1017/pds.2024.20.

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AbstractThe spatial arrangement of objects in residential environments is a crucial indicator of occupant behavior, shedding light on the complex dynamics of their interaction with the interior. This study introduces an object-based graph method for decoding urban home interiors, examining the co-presence of objects to uncover domestic behavioral patterns through indoor imagery analysis. By integrating centrality metrics with objects in graphs, we gain deeper insights into household behaviors, which provide empirical evidence for future interior design.

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Ali, Nadia, and David Peebles. "The Effect of Gestalt Laws of Perceptual Organization on the Comprehension of Three-Variable Bar and Line Graphs." Human Factors: The Journal of the Human Factors and Ergonomics Society 55, no. 1 (2012): 183–203. http://dx.doi.org/10.1177/0018720812452592.

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Objective: We report three experiments investigating the ability of undergraduate college students to comprehend 2 × 2 “interaction” graphs from two-way factorial research designs. Background: Factorial research designs are an invaluable research tool widely used in all branches of the natural and social sciences, and the teaching of such designs lies at the core of many college curricula. Such data can be represented in bar or line graph form. Previous studies have shown, however, that people interpret these two graphical forms differently. Method: In Experiment 1, participants were required

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Vodyaho, Alexander, Nataly Zhukova, Igor Kulikov, and Saddam Abbas. "Using the Context-Sensitive Policy Mechanism for Building Data Acquisition Systems in Large Scale Distributed Cyber-Physical Systems Built on Fog Computing Platforms." Computers 10, no. 8 (2021): 101. http://dx.doi.org/10.3390/computers10080101.

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The article deals with the use of context-sensitive policies in the building of data acquisition systems in large scale distributed cyber-physical systems built on fog computing platforms. It is pointed out that the distinctive features of modern cyber-physical systems are their high complexity and constantly changing structure and behavior, which complicates the data acquisition procedure. To solve this problem, it is proposed to use an approach according to which the data acquisition procedure is divided into two phases: model construction and data acquisition, which allows parallel realizat

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Ding, Junmei, Peng Qian, Jing Ma, Zhiqiang Wang, Yueming Lu, and Xiaqing Xie. "Detect Insider Threat with Associated Session Graph." Electronics 13, no. 24 (2024): 4885. https://doi.org/10.3390/electronics13244885.

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Insider threats pose significant risks to organizational security, often leading to severe data breaches and operational disruptions. While foundational, traditional detection methods suffer from limitations such as labor-intensive rule creation, lack of scalability, and vulnerability to evasion by sophisticated attackers. Recent advancements in graph-based approaches have shown promise by leveraging behavior analysis for threat detection. However, existing methods frequently oversimplify session behaviors and fail to extract fine-grained features, which are critical for identifying subtle mal

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Xu, Qingling, Dawei Zhao, Shumian Yang, Lijuan Xu, and Xin Li. "Correction: Xu et al. Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks. Electronics 2023, 12, 4817." Electronics 13, no. 22 (2024): 4464. http://dx.doi.org/10.3390/electronics13224464.

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Ouyang, Shiyu, Qianlan Bai, Hui Feng, and Bo Hu. "Bitcoin Money Laundering Detection via Subgraph Contrastive Learning." Entropy 26, no. 3 (2024): 211. http://dx.doi.org/10.3390/e26030211.

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The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous, and noisy nature of Bitcoin money laundering. We propose a novel subgraph-based cont

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Wang, Mingjie, Yifan Huo, Junhong Zheng, and Lili He. "SC-TKGR: Temporal Knowledge Graph-Based GNN for Recommendations in Supply Chains." Electronics 14, no. 2 (2025): 222. https://doi.org/10.3390/electronics14020222.

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Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhance

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Guo, Gabriel, Hanbin Zhang, Liuyi Yao, et al. "MSLife." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 4 (2021): 1–35. http://dx.doi.org/10.1145/3494970.

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Treatment for multiple sclerosis (MS) focuses on managing its symptoms (e.g., depression, fatigue, poor sleep quality), varying with specific symptoms experienced. Thus, for optimal treatment, there arises the need to track these symptoms. Towards this goal, there is great interest in finding their relevant phenotypes. Prior research suggests links between activities of daily living (ADLs) and MS symptoms; therefore, we hypothesize that the behavioral phenotype (revealed through ADLs) is closely related to MS symptoms. Traditional approaches to finding behavioral phenotypes which rely on human

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Panigrahi, Satya Sobhan, and Ajay Kumar Jena. "Optimization of Test Cases in Object-Oriented Systems Using Fractional-SMO." International Journal of Open Source Software and Processes 12, no. 1 (2021): 41–59. http://dx.doi.org/10.4018/ijossp.2021010103.

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This paper introduces the technique to select the test cases from the unified modeling language (UML) behavioral diagram. The UML behavioral diagram describes the boundary, structure, and behavior of the system that is fed as input for generating the graph. The graph is constructed by assigning the weights, nodes, and edges. Then, test case sequences are created from the graph with minimal fitness value. Then, the optimal sequences are selected from the proposed fractional-spider monkey optimization (fractional-SMO). The developed fractional-SMO is designed by integrating fractional calculus a

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Shi, Xincong, and Chuangxin Guo. "Novel Real-Time Power System Scheduling Based on Behavioral Cloning of a Grid Expert Strategy with Integrated Graph Neural Networks." Energies 18, no. 8 (2025): 1934. https://doi.org/10.3390/en18081934.

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Amidst the large-scale integration of renewable energy, power grid operations are increasingly characterized by higher levels of uncertainty, challenging the system’s safety and stability. Traditional model-driven dispatch methods are computationally intensive, and recent Reinforcement Learning (RL) techniques struggle with slow training times due to high-dimensional state spaces, while the inability to fully utilize the system’s topology information affects scheduling accuracy. This paper introduces a novel Behavioral Cloning of Grid Expert Strategy with Integrated Graph Neural Networks (GES-

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Minh T. Le, Oliver Harris, Charlotte Bennett, and Fiona Greene. "Behavior Path Analysis for Blockchain Fraud Detection Using Graph Neural Architectures." Frontiers in Applied Physics and Mathematics 2, no. 1 (2025): 13–20. https://doi.org/10.71465/fapm229.

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With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoo

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Obert, James, and Adrian Chavez. "Graph Theory and Classifying Security Events in Grid Security Gateways." International Journal of Semantic Computing 14, no. 01 (2020): 93–105. http://dx.doi.org/10.1142/s1793351x2040005x.

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In recent years, the use of security gateways (SG) located within the electrical grid distribution network has become pervasive. SGs in substations and renewable distributed energy resource aggregators (DERAs) protect power distribution control devices from cyber and cyber-physical attacks. When encrypted communications within a DER network is used, TCP/IP packet inspection is restricted to packet header behavioral analysis which in most cases only allows the SG to perform anomaly detection of blocks of time-series data (event windows). Packet header anomaly detection calculates the probabilit

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Karuza, Elisabeth A., Ari E. Kahn, and Danielle S. Bassett. "Human Sensitivity to Community Structure Is Robust to Topological Variation." Complexity 2019 (February 11, 2019): 1–8. http://dx.doi.org/10.1155/2019/8379321.

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Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and

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Tripathi, Dipty, Shreya Banerjee, and Anirban Sarkar. "Formalization of business workflow with typed attributed graph." International Journal of Web Information Systems 14, no. 3 (2018): 372–98. http://dx.doi.org/10.1108/ijwis-05-2017-0044.

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Purpose Business process workflow is a design conceptualization to automate the sequence of activities to achieve a business goal with involved participants and a predefined set of rules. Regarding this, a formal business workflow model is a prime requisite to implement a consistent and rigorous business process. In this context, majority of the existing research works are formalized structural features and have not focused on functional and behavioral design aspects of business processes. To address this problem, this paper aims to propose a formal model of business process workflow called as

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Wang, Ke, Yanmin Zhu, Tianzi Zang, Chunyang Wang, and Mengyuan Jing. "Review-Enhanced Hierarchical Contrastive Learning for Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 9107–15. http://dx.doi.org/10.1609/aaai.v38i8.28761.

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Designed to establish potential relations and distill high-order representations, graph-based recommendation systems continue to reveal promising results by jointly modeling ratings and reviews. However, existing studies capture simple review relations, failing to (1) completely explore hidden connections between users (or items), (2) filter out redundant information derived from reviews, and (3) model the behavioral association between rating and review interactions. To address these challenges, we propose a review-enhanced hierarchical contrastive learning, namely ReHCL. First, ReHCL constru

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Song, Qijie, Tieming Chen, Tiantian Zhu, Mingqi Lv, Xuebo Qiu, and Zhiling Zhu. "APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining." Applied Sciences 15, no. 11 (2025): 5872. https://doi.org/10.3390/app15115872.

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Advanced Persistent Threats (APTs) challenge cybersecurity due to their stealthy, multi-stage nature. For the provenance graph based on fine-grained kernel logs, existing methods have difficulty distinguishing behavior boundaries and handling complex multi-entity dependencies, which exhibit high false positives in dynamic environments. To address this, we propose a Hypergraph Attention Network framework for APT detection. First, we employ anomaly node detection on provenance graphs constructed from kernel logs to select seed nodes, which serve as starting points for discovering overlapping beh

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Wei, Hanying. "Research on multi-role classification task of online mall based on heterogeneous graph neural network." Applied and Computational Engineering 39, no. 1 (2024): 63–70. http://dx.doi.org/10.54254/2755-2721/39/20230581.

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With the rapid development of e-commerce, online shopping malls have become an indispensable part of daily life. In order to better meet the needs of consumers, marketplace platforms need to accurately identify and categorize different user personas to provide personalized services and recommendations. In traditional role classification methods, basic information and behavioral data of users are typically used for classification. However, this approach often ignores the complex relationships between users and multiple heterogeneous data such as goods, reviews, social networks, and more. Theref

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Reading Turchioe, Meghan, Lisa V. Grossman, Annie C. Myers, Dawon Baik, Parag Goyal, and Ruth M. Masterson Creber. "Visual analogies, not graphs, increase patients' comprehension of changes in their health status." Journal of the American Medical Informatics Association 27, no. 5 (2020): 677–89. http://dx.doi.org/10.1093/jamia/ocz217.

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Abstract Objectives Patients increasingly use patient-reported outcomes (PROs) to self-monitor their health status. Visualizing PROs longitudinally (over time) could help patients interpret and contextualize their PROs. The study sought to assess hospitalized patients' objective comprehension (primary outcome) of text-only, non-graph, and graph visualizations that display longitudinal PROs. Materials and Methods We conducted a clinical research study in 40 hospitalized patients comparing 4 visualization conditions: (1) text-only, (2) text plus visual analogy, (3) text plus number line, and (4)

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Zhang, Guohua, and Weijia Wang. "Hierarchic Entropy: An Information Theoretic Measure of Evolutionary Robotic Behavioral Diversity." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1751002. http://dx.doi.org/10.1142/s0218001417510028.

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This paper investigates the design of information theoretic-based fitness function for embedded evolutionary robotics (ERs). Such fitness relies on the assumption that interesting behaviors result in a high sensorimotor (individual) diversity. The current simple entropy as a diversity metric only considers individuals’ difference but ignores their spatial relationship. The sensorimotor stream can be analyzed to construct a simple directed graph that has unique entry and exit nodes. This paper proposes a hierarchic entropy as a diversity metric by incorporating the simple entropy and the spatia

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Jyoti, Chand, Kalkundiya A., Semwal V, Dhariyal N.S., and Pathak Bipin Chandra. "Circannual Behavioral Study in Indian Hill Trout Fish Barilius bendelisis (Hamilton)." International Journal of Zoological Investigations 08, no. 02 (2022): 451–58. http://dx.doi.org/10.33745/ijzi.2022.v08i02.055.

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In the present study, we collected the fishes from the lake throughout the year for their behavioral study with the help of Ethovision XT 13 (Noldus, Infotech, Wageningen, the Netherlands). Different behavioral parameters such as mobility, turn angle, meander, and area of exploration were recorded by using the Basler camera and software. The behavioral profile of each fish was scored for 300 sec at the rate of 25 frame/sec. The digitized scored data was analyzed by using the Graph pad prism. All the parameters showed a highly significant difference at P<0.05.

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Bou-Harb, Elias, Mourad Debbabi, and Chadi Assi. "Big Data Behavioral Analytics Meet Graph Theory: On Effective Botnet Takedowns." IEEE Network 31, no. 1 (2017): 18–26. http://dx.doi.org/10.1109/mnet.2016.1500116nm.

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Zhang, Zun-dong, Yan-fang Yang, Wenjiao Qi, Abderrahim Chariete, and Xing-xiang Lin. "A Cellular Automata Traffic Flow Model considering Bus Lane Changing Behavior with Scheduling Parameters." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/348582.

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According to different driving behavioral characteristics of bus drivers, a cellular automata traffic model considering the bus lane changing behavior with scheduling parameters is proposed in this paper. Traffic bottleneck problems caused by bus stops are simulated in multiple lanes roads with no-bay bus stations. With the mixed traffic flow composed of different bus arrival rate, flow-density graph, density distribution graph, and temporal-spatial graph are presented. Furthermore, the mixed traffic flow characteristics are analyzed. Numerical experiment results show that the proposed model c

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Abu-Abed, Fares, and Sergey Zhironkin. "New Game Artificial Intelligence Tools for Virtual Mine on Unreal Engine." Applied Sciences 13, no. 10 (2023): 6339. http://dx.doi.org/10.3390/app13106339.

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Currently, the gamification of virtual reality for training miners, especially for emergencies, and designing the extraction of minerals in difficult technological conditions has been embodied in the Virtual Mine software and hardware. From a software development point of view, Virtual Mine is indistinguishable from other virtual reality games, and this offers a chance to use the potential of rapidly developing game software in mining, including engines, 3D modeling tools, audio editors, etc., to solve a wide range of game development tasks. The chosen direction will optimize the work of devel

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Liu, Runbo, Lili He, and Junhong Zheng. "GLARA: A Global–Local Attention Framework for Semantic Relation Abstraction and Dynamic Preference Modeling in Knowledge-Aware Recommendation." Applied Sciences 15, no. 12 (2025): 6386. https://doi.org/10.3390/app15126386.

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Knowledge graph-enhanced recommendation has gained increasing attention for its ability to provide structured semantic context. However, most existing approaches struggle with two critical challenges: the sparsity of long-tail relations in knowledge graphs and the lack of adaptability to users’ dynamic preferences. In this paper, we propose GLARA, a novel recommendation framework that combines semantic abstraction and behavioral adaptation through a two-stage modeling process. First, a Virtual Relational Knowledge Graph (VRKG) is constructed by clustering semantically similar relations into hi

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Zheng, Nan, Meng Sun, and Ye Yang. "Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5398932.

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In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students’ sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper pres

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Peng, Xingyu, Zhenkun Zhou, Chong Zhang, and Ke Xu. "Online Social Behavior Enhanced Detection of Political Stances in Tweets." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 1207–19. http://dx.doi.org/10.1609/icwsm.v18i1.31383.

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Public opinion plays a pivotal role in politics, influencing political leaders' decisions, shaping election outcomes, and impacting policy-making processes. In today's digital age, the abundance of political discourse available on social media platforms has become an invaluable resource for analyzing public opinion. This paper focuses on the task of detecting political stances in the context of the 2020 US presidential election. To facilitate this research, we curate a substantial dataset sourced from Twitter, annotated using hashtags as indicators of political polarity. In our approach, we co

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Tripathi, Akshay, Ankush Kumar Gaur, and Sweta Sri. "Implementation and Analysis of Social Network Graph in Interpersonal Network." Jurnal Ilmu Komputer 13, no. 2 (2020): 5. http://dx.doi.org/10.24843/jik.2020.v13.i02.p03.

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Social graph describes the graphical model of users and how they are related to each other online. Social network consists of a set of nodes (sometimes referred to as actors or vertices in graph theory) connected via some type of relations which are known as edges. Actors are the smallest unit of the network. It can be Persons, Organizations, and Families etc. Relations can be of many types such as directed, undirected, and weighted. Social network analysis consists of two phases. One is data collection phase and another is analysis phase. Data is collected with the help of surveys, Social sit

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Nigro, Salvatore, Benedetta Tafuri, Daniele Urso, et al. "Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia." Brain Sciences 11, no. 2 (2021): 192. http://dx.doi.org/10.3390/brainsci11020192.

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Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical co

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Tabatabaie, Mahan, Suining He, and Kang G. Shin. "Cross-Modality Graph-based Language and Sensor Data Co-Learning of Human-Mobility Interaction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 3 (2023): 1–25. http://dx.doi.org/10.1145/3610904.

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Learning the human--mobility interaction (HMI) on interactive scenes (e.g., how a vehicle turns at an intersection in response to traffic lights and other oncoming vehicles) can enhance the safety, efficiency, and resilience of smart mobility systems (e.g., autonomous vehicles) and many other ubiquitous computing applications. Towards the ubiquitous and understandable HMI learning, this paper considers both "spoken language" (e.g., human textual annotations) and "unspoken language" (e.g., visual and sensor-based behavioral mobility information related to the HMI scenes) in terms of information

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Zhang, Mingliang, Xiangyang Luo, Pei Zhang, Hao Li, Yi Zhang, and Lingling Li. "High-Capacity Robust Behavioral Steganography Method Based on Timestamp Modulation across Social Internet of Things." Security and Communication Networks 2021 (December 31, 2021): 1–16. http://dx.doi.org/10.1155/2021/6351144.

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Social Internet of Things (SIoT) is an emerging field that combines IoT and Internet, which can provide many novel and convenient application scenarios but still faces challenges in data privacy protection. In this paper, we propose a robust behavioral steganography method with high embedding capacity across social networks based on timestamp modulation. Firstly, the IoT devices on the sending end modulate the secret message to be embedded into a timestamp by using the common property on social networks. Secondly, the accounts of multiple social networks are used as the vertices, and the times

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Mu, Xiaodong, Zhaoju Zeng, Danyao Shen, and Bo Zhang. "Multi-Feature Behavior Relationship for Multi-Behavior Recommendation." Applied Sciences 12, no. 24 (2022): 12909. http://dx.doi.org/10.3390/app122412909.

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Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specif

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