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Change Detection in Dynamic Attributed Networks [article]

Isuru Udayangani Hewapathirana
2020 arXiv   pre-print
In this survey we provide an overview of some of the existing change detection methods that utilize attribute information.  ...  It is a challenging problem because it involves a time sequence of attributed graphs, each of which is usually very large and can contain many attributes attached to the vertices and edges, resulting in  ...  Roger Jarquin from the School of Mathematics and Statistics, University of Canterbury, New Zealand, for providing insight and expertise that greatly assisted this work.  ... 
arXiv:2001.04734v1 fatcat:ohzwafwe5bfe5i4ox6uvlnn6au

Change Point Methods on a Sequence of Graphs [article]

Daniele Zambon, Cesare Alippi, Lorenzo Livi
2018 arXiv   pre-print
In order to cover a large class of applications, we consider the general family of attributed graphs where both topology (number of vertexes and edge configuration) and related attributes are allowed to  ...  Given a finite sequence of graphs, e.g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process  ...  project 200021_172671: "ALPSFORT: A Learning graPh-baSed framework FOr cybeR-physical sysTems".  ... 
arXiv:1805.07113v2 fatcat:ktj2ftkvvrdlphugmxqj77pvyq

Explainable Video Action Reasoning via Prior Knowledge and State Transitions [article]

Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli
2019 arXiv   pre-print
Given a video sequence, we first generate a scene graph on each frame to represent concerned objects, attributes and relationships.  ...  Finally, by sequentially examining each state transition in the video graph, our method can detect and explain how those actions are executed with prior knowledge, just like the logical manner of thinking  ...  The AAR model is used to detect the attribute-transition of a node in video graph, such as "open a microwave" (the attribute changes from closed to open).  ... 
arXiv:1908.10700v1 fatcat:itbcavk37fgkfmnhn5syzze6iy

Malicious Detection Based on ReliefF and Boosting Multidimensional Features

Yang Xia Luo
2015 Journal of Communications  
sequence) and two features (system call graph and function call graph) which combines the semantic features to reflect the behaviour characteristic of the malware, and then selects important feature vectors  ...  Abstract-Aiming at the problem of large overhead and low accuracy on the identification of obfuscated and malicious code, a new algorithm is proposed to detect malicious code by identifying multidimensional  ...  Table II , when changing the similarity thresholds of directed graph, the detection results first becomes large then small, although not in proportion to grow, but the detection result is preferably when  ... 
doi:10.12720/jcm.10.11.910-917 fatcat:dgrfigycrjgwjpf6g2wtt4vxg4

Detecting Change Patterns in Aspect Oriented Software Evolution: Rule-based Repository Analysis

Hanene Cherait, Nora Bounour
2014 International Journal of Software Engineering and Its Applications  
In this last, every version of the software (graph) is the set of rewrite rule sequences, where, every rewrite rule sequence presents a specific change request.  ...  Practically, we use the program representation presented in our previous work [7], where, the AspectJ program is converted to an attributed colored graph.  ...  ., [26] treat the detection of change patterns in AspectJ programs.  ... 
doi:10.14257/ijseia.2014.8.1.22 fatcat:4u2hyqvvyvd6laiucyzd5cubpy

Graph similarity learning for change-point detection in dynamic networks [article]

Deborah Sulem, Henry Kenlay, Mihai Cucuringu, Xiaowen Dong
2022 arXiv   pre-print
In this work, we consider dynamic networks that are temporal sequences of graph snapshots, and aim at detecting abrupt changes in their structure.  ...  We show on synthetic and real data that our method enjoys a number of benefits: it is able to learn an adequate graph similarity function for performing online network change-point detection in diverse  ...  similarity function and to detect change-points in a network sequence using the latter.  ... 
arXiv:2203.15470v1 fatcat:dax7fxusazetva2a6wb4z35hky

A supervised approach to time scale detection in dynamic networks [article]

Benjamin Fish, Rajmonda S. Caceres
2017 arXiv   pre-print
However, the choice can make a big difference in the properties of the dynamic network. This is the time scale detection problem.  ...  Therefore the time scale detection problem should not be handled independently from the rest of the analysis of the network.  ...  In this paper, we consider link prediction, attribute prediction, and change point detection.  ... 
arXiv:1702.07752v1 fatcat:ne4sirxu4ngmfaaema4u5l6gpy

Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

Ya-nan Wang, Jian Wang, Xiaoshi Fan, Yafei Song
2020 IEEE Access  
At each moment, using the multi-dimensional attribute entropy values as vertices, we construct complete graphs using amplitudes of the change in entropy values and edge weights between vertices defined  ...  by similarity, and establish an intuitionistic fuzzy time series graph of the traffic data in the time dimension.  ...  Attributes that can effectively reflect changes in network traffic constitute a feature sequence, and their information entropy is a metric of anomaly.  ... 
doi:10.1109/access.2020.2983986 fatcat:wzivibootvfh5bahjdgyxa7poe

A Supervised Learning Approach to Link Prediction in Dynamic Networks [chapter]

Shuai Xu, Kai Han, Naiting Xu
2018 Lecture Notes in Computer Science  
We call this the windowing detection problem. In previous work, this problem is often solved with a heuristic as an unsupervised task.  ...  While this choice is often picked by hand, or left up to the technology that is collecting the data, the choice can make a big difference in the properties of the network.  ...  Graphscope detects change points by estimating the times where segmenting the graph sequence at those times maximizes compressibility.  ... 
doi:10.1007/978-3-319-94268-1_70 fatcat:jmrndgacxbb3thzx2pit2fa2ce

Graph-Based Policy Change Detection and Implementation in SDN

Mudassar Hussain, Nadir Shah, Ali Tahir
2019 Electronics  
In this regard, we used abstractions to formalize and detect network policies with the help of multi-attributed graphs.  ...  In this research work, we deal with the inefficiencies of policy change detection with respect to access time, cost and space.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics8101136 fatcat:bxrb4xqje5hpdhgz33vfwzt6sm

Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments

Jongmo Kim, Mye Sohn
2022 Sensors  
These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression.  ...  The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22041545 pmid:35214446 pmcid:PMC8880711 fatcat:ii4uinx33bbh7kajzrtuc5vgfq

Concept Drift and Anomaly Detection in Graph Streams

Daniele Zambon, Cesare Alippi, Lorenzo Livi
2018 IEEE Transactions on Neural Networks and Learning Systems  
Here, we consider stochastic processes generating graphs and propose a methodology for detecting changes in stationarity of such processes.  ...  In addition, we provide a specific implementation of the methodology and evaluate its effectiveness on several detection problems involving attributed graphs representing biological molecules and drawings  ...  in sequences of graphs.  ... 
doi:10.1109/tnnls.2018.2804443 pmid:29994077 fatcat:hbulbnosajgptfemcegtenwomi

GFD: A Weighted Heterogeneous Graph Embedding Based Approach for Fraud Detection in Mobile Advertising

Jinlong Hu, Tenghui Li, Yi Zhuang, Song Huang, Shoubin Dong
2020 Security and Communication Networks  
In this paper, we propose a novel weighted heterogeneous graph embedding and deep learning-based fraud detection approach, namely, GFD, to identify fraudulent apps for mobile advertising.  ...  to learn node representations (graph-based features) from the graph; (ii) we use a time window based statistical analysis method to extract intrinsic features (attribute-based features) from the tabular  ...  Related Work Our work is related to existing studies on attribute-based fraud detection and graph-based fraud detection with machine learning. e challenges of fraud detection problem in mobile advertising  ... 
doi:10.1155/2020/8810817 fatcat:6weqvgg3t5aphftf2grum44aoy

An investigation of Hebbian phase sequences as assembly graphs

Daniel G. Almeida-Filho, Vitor Lopes-dos-Santos, Nivaldo A. P. Vasconcelos, José G. V. Miranda, Adriano B. L. Tort, Sidarta Ribeiro
2014 Frontiers in Neural Circuits  
Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior.  ...  Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences.  ...  EXP WK graphs were detected as different from POST WK graphs by 3 attributes.  ... 
doi:10.3389/fncir.2014.00034 pmid:24782715 pmcid:PMC3986516 fatcat:u7rasdypzvfjxm4bon4ht24cpi

Ontology Change Management and Identification of Change Patterns

Muhammad Javed, Yalemisew M. Abgaz, Claus Pahl
2013 Journal on Data Semantics  
In this paper, we propose a fourphase process that covers the operationalization, representation and detection of higherlevel changes in ontology evolution life cycle.  ...  In this sense, the application and representation of ontology changes in terms of higher-level change operations can describe more meaningful semantics behind the applied change.  ...  Acknowledgements This research is supported by Science Foundation Ireland (Grant 07/CE/I1142) as part of the Centre for Next Generation Localisation at Dublin City University.  ... 
doi:10.1007/s13740-013-0024-2 fatcat:legjsad4mjd4jocbel5c7ijkpy
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