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Temporal Patterns Discovery of Evolving Graphs for Graph Neural Network (GNN)-based Anomaly Detection in Heterogeneous Networks

Jongmo Kim, Kunyoung Kim, Gi-Yoon Jeon, Mye M. Sohn
2022 Journal of Internet Services and Information Security  
As a result, we can obtain an evolving graph that is simplified and temporal patternsenhanced from original networks. It is used an input graph for a GNN-based anomaly detection model.  ...  To show the superiority of the proposed framework, we conduct experiments and evaluations on 8 real-world datasets with anomaly labels with comparative state-of-the-art models of graph-based anomaly detection  ...  Acknowledgements Temporal patterns discovery of evolving graphs for GNN-based anomaly detection J. Kim, K. Kim, G. Jeon, and M.  ... 
doi:10.22667/jisis.2022.02.28.072 dblp:journals/jisis/KimKJS22 fatcat:uncpemjenbgavni65vrnhzszzy

Discovery of temporal patterns from process instances

San-Yih Hwang, Chih-Ping Wei, Wan-Shiou Yang
2004 Computers in industry (Print)  
In this study, we formally defined the temporal pattern discovery problem, and developed and evaluated three different temporal pattern discovery algorithms, namely TP-Graph, TP-Itemset and TP-Sequence  ...  Discovery of temporal patterns can be applied to various application domains to support crucial business decision-making.  ...  hash function to the d-th vertex in the temporal sequence of G.  ... 
doi:10.1016/j.compind.2003.10.006 fatcat:em3sz6i2sbfkdk5szf7re6qsmu

Locating Temporal Functional Dynamics of Visual Short-Term Memory Binding using Graph Modular Dirichlet Energy

Keith Smith, Benjamin Ricaud, Nauman Shahid, Stephen Rhodes, John M. Starr, Augustin Ibáñez, Mario A. Parra, Javier Escudero, Pierre Vandergheynst
2017 Scientific Reports  
To uncover functional deficits of AD in these tasks it is meaningful to first study unimpaired brain function.  ...  Particularly, we introduce and implement a novel technique named Modular Dirichlet Energy (MDE) which allows robust and flexible analysis of the functional network with unprecedented temporal precision  ...  NS was supported by SNF grant 200021 154350/1 for the project "Towards signal processing on graphs".  ... 
doi:10.1038/srep42013 pmid:28186173 pmcid:PMC5301217 fatcat:sihsasm4p5dqhm7brtsvkfwoxq

Identifying significant temporal variation in time course microarray data without replicates

Stephen C Billups, Margaret C Neville, Michael Rudolph, Weston Porter, Pepper Schedin
2009 BMC Bioinformatics  
The proposed algorithm identifies a larger percentage of the time-dependent genes for a given false discovery rate.  ...  The simulated data consists both of genes with known temporal dependencies, and genes from a null distribution.  ...  The graphs in Figure 2 correspond to cases where there is a temporal dependency whose frequency is relatively low, and the graphs in Figure 3 correspond to higher frequency temporal dependencies.  ... 
doi:10.1186/1471-2105-10-96 pmid:19323838 pmcid:PMC2682797 fatcat:udlvwzwudvfhpltdcpcagljd4a

Discovering Temporal Communities from Social Network Documents

Ding Zhou, Isaac Councill, Hongyuan Zha, C. Lee Giles
2007 Seventh IEEE International Conference on Data Mining (ICDM 2007)  
This paper studies the discovery of communities from social network documents produced over time, addressing the discovery of temporal trends in community memberships.  ...  We first formulate static community discovery at a single time period as a tripartite graph partitioning problem.  ...  Then we ran the temporal community discovery (t-par) algorithm with k = 4 with various settings of λ.  ... 
doi:10.1109/icdm.2007.56 dblp:conf/icdm/ZhouCZG07 fatcat:sbo7ezh4vvbs5m4x2wghf4pbua

Causal Discovery in Hawkes Processes by Minimum Description Length [article]

Amirkasra Jalaldoust, Katerina Hlavackova-Schindler, Claudia Plant
2022 arXiv   pre-print
The synthetic experiments demonstrate superiority of our method incausal graph discovery compared to the baseline methods with respect to the size of the data.  ...  Discovery of the underlying influence network among the dimensions of multi-dimensional temporal processes is of high importance in disciplines where a high-frequency data is to model, e.g. in financial  ...  In its general form, the NML distribution and the MDL estimators depend on a function v : Θ → R + 0 named luckiness function.  ... 
arXiv:2206.06124v1 fatcat:yiurru7osfe3tnno43z2beb7ki

Mining Topological Dependencies of Recurrent Congestion in Road Networks

Nicolas Tempelmeier, Udo Feuerhake, Oskar Wage, Elena Demidova
2021 ISPRS International Journal of Geo-Information  
This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network  ...  The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the  ...  We provide an open-source implementation of the ST-DISCOVERY algorithms under the MIT-license (, accessed on 19 March 2021).  ... 
doi:10.3390/ijgi10040248 fatcat:2fnygc6zffdypo2w5674k5rzny

Probabilistic graphical models for climate data analysis

Arindam Banerjee
2011 Proceedings of the 2011 workshop on Climate knowledge discovery - CKD '11  
• Key Challenges -High-dimensional dependent data, small sample size -Spatial and temporal dependencies, temporal lags -Oscillations with frequency and phase variations -Important variables are unreliable  ...  Graphs -A directed graph between random variables -Example: Bayesian networks, Hidden Markov Models -Joint distribution is a product of P(child|parents) • Undirected Graphs -An undirected graph  ...  Results: Droughts starting in 1960-70s The prolonged drought in Sahel in the 1970s Drought in India and Bangladesh in the 1960s Major Droughts: 1901 Droughts: -2006 18 Learning dependencies , 2002  ... 
doi:10.1145/2110230.2110235 fatcat:p3gxemm6oze7pnazykks66yngi

Proxy-based Asynchronous Multicast for Efficient On-demand Media Distribution

Yi Cui, Klara Nahrstedt, Ragunathan Rajkumar
2003 Multimedia Computing and Networking 2003  
In this paper, we focus on the problem of efficient media distribution. We first propose a temporal dependency model to formalize the temporal relations among asynchronous media requests.  ...  Based on this model, we propose the concept of Media Distribution Graph (MDG), which represents the dependencies among all asynchronous requests in the proxy network.  ...  Views and conclusions of this paper are those of authors, which should not be interpreted as representing the official policies, either expressed or implied, of the funding agencies.  ... 
doi:10.1117/12.484078 fatcat:kscpt23ld5ft3l73izjl7s46z4

Discovering Complex Knowledge in Massive Building Operational Data Using Graph Mining for Building Energy Management

Cheng Fan, Mengjie Song, Fu Xiao, Xue Xue
2019 Energy Procedia  
On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios).  ...  ., graph generation based on building operational data and knowledge discovery from graph data.  ...  Acknowledgements The authors gratefully acknowledge the support of this research by the Natural Science Foundation of SZU (grant no. 2017061) and the Research Grant Council (RGC) of the Hong Kong SAR (  ... 
doi:10.1016/j.egypro.2019.01.378 fatcat:a2e4e7eirbddjmu726qzltaxym

Causal Discovery with Attention-Based Convolutional Neural Networks

Meike Nauta, Doina Bucur, Christin Seifert
2019 Machine Learning and Knowledge Extraction  
Our framework learns temporal causal graphs, which can include confounders and instantaneous effects.  ...  We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/make1010019 fatcat:z7y26n3czngopcq5qc3oo7d2ju

Causal discovery from conditionally stationary time-series [article]

Carles Balsells Rodas, Ruibo Tu, Hedvig Kjellstrom
2021 arXiv   pre-print
In this work we aim for causal discovery in a more general class of scenarios, scenes with non-stationary behavior over time.  ...  Non-stationarity is modeled as stationarity conditioned on an underlying variable, a state, which can be of varying dimension, more or less hidden given observations of the scene, and also depend more  ...  We propose a method (see Section 3) for causal discovery from time-series observations of systems where the underlying causal graph changes depending on a state variable.  ... 
arXiv:2110.06257v1 fatcat:kbjmpxjm4zcgrmxxr33hdlzeim

Deep into Hypersphere: Robust and Unsupervised Anomaly Discovery in Dynamic Networks

Xian Teng, Muheng Yan, Ali Mert Ertugrul, Yu-Ru Lin
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
localized in spatial and temporal context.  ...  We propose a unified anomaly discovery framework "DeepSphere" that simultaneously meet the above two requirements -- identifying the anomalous cases and further exploring the cases' anomalous structure  ...  Acknowledgements This work is part of the research supported from NSF #1634944, #1637067, and #1739413.  ... 
doi:10.24963/ijcai.2018/378 dblp:conf/ijcai/TengYEL18 fatcat:6axag6wiprbpviwkvagii5545q

Community Discovery in Dynamic Networks

Giulio Rossetti, Rémy Cazabet
2018 ACM Computing Surveys  
The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches.  ...  Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery.  ...  Acknowledgement We thank Jane Carlen for her feedback that helped us correct some errors in the description of some methods.  ... 
doi:10.1145/3172867 fatcat:x6gcg42j3raklfr2f2y3ou5u44

Toward Learning Graphical and Causal Process Models

Christopher Meek
2014 Conference on Uncertainty in Artificial Intelligence  
We describe an approach to learning causal models that leverages temporal information. We posit the existence of a graphical description of a causal process that generates observations through time.  ...  We explore assumptions connecting the graphical description with the statistical process and what one can infer about the causal structure of the process under these assumptions.  ...  Acknowledgments Thanks to Asela Gunawardana and two anonymous reviewers for their comments on an earlier draft of this paper.  ... 
dblp:conf/uai/Meek14 fatcat:3m6xm4gn4rgczkmkqw3e2lyuhe
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