SpotLight

Eswaran, Dhivya; Faloutsos, Christos; Guha, Sudipto; Mishra, Nina

doi:10.1145/3219819.3220040

Cited by 106 publications

(13 citation statements)

References 26 publications

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“… Chan et al (2008) detected spatiotemporal changes that are correlated in dynamic graphs. SpotLight ( Eswaran et al 2018 ) spots large dense subgraphs that appear or disappear suddenly. SDREGION ( Wong et al 2018 ) finds subgraphs such that their densities monotonically increase or decrease across time.…”

Section: Introductionmentioning

confidence: 99%

USNAP: fast unique dense region detection and its application to lung cancer

Wong,

Pastrello,

Kotlyar

et al. 2023

Bioinformatics

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Motivation Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this paper, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains. Results There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer (NSCLC) gene expression datasets. Stages in NSCLC were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m + mclognc + nclognc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph. Availability The code of USNAP is available at https://www.cs.utoronto.ca/∼juris/data/USNAP22. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

USNAP: fast unique dense region detection and its application to lung cancer

Wong,

Pastrello,

Kotlyar

et al. 2023

Bioinformatics

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“…Within dynamic networks, the definition of an anomalous object varies widely depending on the specific application context. Based on the diverse nature of anomalies that can occur in such evolving structures, the scope of detection tasks can range from identifying abnormal nodes [12], [16]- [18] and edges [5], [19]- [21] to pinpointing anomalous subgraphs [22], [23]. Early approaches mainly leverage the shallow mechanisms to detect anomalies in dynamic graphs.…”

Section: A Anomaly Detection In Dynamic Graphsmentioning

confidence: 99%

“…MTHL [12] distinguishes normal and anomalous nodes according to their distances to the learned hypersphere center. SpotLight [23] guarantees a large mapped distance between anomalous and normal graphs in the sketch space with a randomized sketching technique.…”

Section: A Anomaly Detection In Dynamic Graphsmentioning

confidence: 99%

Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes

Chang

Liu

2021

IEEE Trans. Multimedia

124

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Anomaly detection in dynamic graphs presents a significant challenge due to the temporal evolution of graph structures and attributes. The conventional approaches that tackle this problem typically employ an unsupervised learning framework, capturing normality patterns with exclusive normal data during training and identifying deviations as anomalies during testing. However, these methods face critical drawbacks: they either only depend on proxy tasks for general representation without directly pinpointing normal patterns, or they neglect to differentiate between spatial and temporal normality patterns, leading to diminished efficacy in anomaly detection. To address these challenges, we introduce a novel Spatial-Temporal memories-enhanced graph autoencoder (STRIPE). Initially, STRIPE employs Graph Neural Networks (GNNs) and gated temporal convolution layers to extract spatial features and temporal features, respectively. Then STRIPE incorporates separate spatial and temporal memory networks, which capture and store prototypes of normal patterns, thereby preserving the uniqueness of spatial and temporal normality. After that, through a mutual attention mechanism, these stored patterns are then retrieved and integrated with encoded graph embeddings. Finally, the integrated features are fed into the decoder to reconstruct the graph streams which serve as the proxy task for anomaly detection. This comprehensive approach not only minimizes reconstruction errors but also refines the model by emphasizing the compactness and distinctiveness of the embeddings in relation to the nearest memory prototypes. Through extensive testing, STRIPE has demonstrated a superior capability to discern anomalies by effectively leveraging the distinct spatial and temporal dynamics of dynamic graphs, significantly outperforming existing methodologies, with an average improvement of 15.39% on AUC values.

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“…queries, like time-stamped nodes [6,7] or entire graph snapshots [8][9][10][11], have also been proposed. These algorithms also vary in the way they define an anomaly and use the context.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms also vary in the way they define an anomaly and use the context. Namely, nodes may be deemed abnormal if they suddenly change their centrality [6] or communication counts [7], while graphs may be considered abnormal if they have sudden densifications [9], spectrum changes [10], or community re-configurations [11], to list some examples.…”

Section: Introductionmentioning

confidence: 99%

MAD: Multi-Scale Anomaly Detection in Link Streams

Bautista,

Brisson,

Bothorel

et al. 2024

Proceedings of the 17th ACM International Conference on Web Search and Data Mining

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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SpotLight

Cited by 106 publications

References 26 publications

USNAP: fast unique dense region detection and its application to lung cancer

USNAP: fast unique dense region detection and its application to lung cancer

Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes

MAD: Multi-Scale Anomaly Detection in Link Streams

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