Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Benzi, Kirell; Ricaud, Benjamin

doi:10.1109/tsipn.2016.2524500

Cited by 13 publications

(10 citation statements)

References 37 publications

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“…Second, a temporal pattern, a set of time-series, that reflects the anomaly evolution on each node. Tracking these types of patterns has a number of applications [3,5,16], although they have not yet been linked to the field of anomaly detection. Enron email dataset.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory

Miz

Benjamin

Benzi

2019

The World Wide Web Conference

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In this work, we propose a new, fast and scalable method for anomaly detection in large time-evolving graphs. It may be a static graph with dynamic node attributes (e.g. time-series), or a graph evolving in time, such as a temporal network. We define an anomaly as a localized increase in temporal activity in a cluster of nodes. The algorithm is unsupervised. It is able to detect and track anomalous activity in a dynamic network despite the noise from multiple interfering sources.We use the Hopfield network model of memory to combine the graph and time information. We show that anomalies can be spotted with a good precision using a memory network. The presented approach is scalable and we provide a distributed implementation of the algorithm.To demonstrate its efficiency, we apply it to two datasets: Enron Email dataset and Wikipedia page views. We show that the anomalous spikes are triggered by the real-world events that impact the network dynamics. Besides, the structure of the clusters and the analysis of the time evolution associated with the detected events reveals interesting facts on how humans interact, exchange and search for information, opening the door to new quantitative studies on collective and social behavior on large and dynamic datasets.

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Section: Related Workmentioning

confidence: 99%

Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory

Miz

Benjamin

Benzi

2019

The World Wide Web Conference

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“…[18], these graphs are suitable to analyze the quality of a communication protocol, since its history is made explicitly of a sequence of graph topologies. A multilayer graph [19] is an evolving graph made of layers which are distinct copies of the main spatial graph, i.e., a graph in which nodes or edges are spatially located according to a certain metric. Each layer is then connected to its neighbor according to some measure of time (e.g., causality, probability, etc.)…”

Section: Evolving Entangled Hypergraphsmentioning

confidence: 99%

Modeling tripartite entanglement in quantum protocols using evolving entangled hypergraphs

Anticoli

Gharahi

2018

Int. J. Quantum Inform.

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The notion of entanglement is the most well-known nonclassical correlation in quantum mechanics and a fundamental resource in quantum information and computation. This correlation, which is displayed by certain classes of quantum states, is of utmost importance when dealing with protocols such as quantum teleportation, quantum cryptography, and quantum key distribution. In this paper, we exploit a classification of tripartite entanglement by introducing the concepts of entangled hypergraph and evolving entangled hypergraph as data structures suitable to model quantum protocols which use entanglement. Finally, we present a few examples to provide applications of this model.

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“…Graph is visually represented and it is sensitive to creative interventions in the art perspective. Graph form is unpredictable enough from the aspect of visual forms, but from the point of big data it is very simple to represent it (Benzi et al, 2015;Diestel, 2000). We considered that the basic characteristic of data is information and that the basic property of this visualization is communication.…”

Section: Introductionmentioning

confidence: 99%

Graph structures for data visualizations

Janićijević¹,

Nikolić²

2021

Serb J Engin Manag

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Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

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Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Cited by 13 publications

References 37 publications

Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory

Anomaly Detection in the Dynamics of Web and Social Networks Using Associative Memory

Modeling tripartite entanglement in quantum protocols using evolving entangled hypergraphs

Graph structures for data visualizations

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