HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

LaRock, Timothy; Nanumyan, Vahan; Scholtes, Ingo; Casiraghi, Giona; Eliassi-Rad, Tina; Schweitzer, Frank

doi:10.1137/1.9781611976236.52

Cited by 15 publications

(18 citation statements)

References 48 publications

(55 reference statements)

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“…Remark 5.4. We note that the matrices W k in the statement of theorem 5.3 correspond to the adjacency matrices of the kth order De Bruijn graphs of paths in the network; see [46].…”

Section: And L K−1 and R K−1 Are The Source And Target Matrix Of The mentioning

confidence: 99%

Beyond non-backtracking: non-cycling network centrality measures

2020

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Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks.

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“…Remark 5.4. We note that the matrices W k in the statement of theorem 5.3 correspond to the adjacency matrices of the kth order De Bruijn graphs of paths in the network; see [46].…”

Section: And L K−1 and R K−1 Are The Source And Target Matrix Of The mentioning

confidence: 99%

Beyond non-backtracking: non-cycling network centrality measures

2020

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“…They are defined as small graphs composed of walks on them. Yet another usage of temporal motifs can be found in 51 , in which authors use similar concept to ours to detect anomalies in time series on networks.…”

Section: Counting Causal Motifsmentioning

confidence: 97%

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

Barjašić

Štefančić

Pribičević

et al. 2021

Preprint

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Motivated by the detection of cascades of defaults in economy, we developed a detection framework for an endogenous spreading based on causal motifs we define in this paper. We assume that the change of state of a vertex can be triggered by an endogenous or an exogenous event, that the underlying network is directed and that times when vertices changed their states are available. In addition to the data of company defaults, we also simulate cascades driven by different stochastic processes on different synthetic networks. We show that some of the smallest motifs can robustly detect endogenous spreading events. Finally, we apply the method to the data of defaults of Croatian companies and observe the time window in which an endogenous cascade was likely happening.

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“…Paths were constructed from public transit data for path-based analysis of the London Tube in [15,25]. However, the method for constructing the paths was to compute shortest paths through the combined network of routes, which did not take the number of transfers into account.…”

Section: Paths In Transportation Networkmentioning

confidence: 99%

A Path-based Approach to Analyzing the Global Liner Shipping Network

LaRock¹,

Xu²,

Eliassi-Rad³

2021

Preprint

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The maritime shipping network is the backbone of global trade. Data about the movement of cargo through this network comes in various forms, from ship-level Automatic Identification System (AIS) data, to aggregated bilateral trade volume statistics. Multiple network representations of the shipping system can be derived from any one data source, each of which has advantages and disadvantages. In this work, we examine data in the form of liner shipping service routes, a list of walks through the port-to-port network aggregated from individual shipping companies by a large shipping logistics database. This data is inherently sequential, in that each route represents a sequence of ports called upon by a cargo ship. Previous work has analyzed this data without taking full advantage of the sequential information. Our contribution is to develop a path-based methodology for analyzing liner shipping service route data, computing navigational trajectories through the network that respect the routes and comparing these paths with those computed using other network representations of the same data. We further use these trajectories to re-analyze the role of a previously-identified structural core through the network, as well as to define and analyze a measure of betweenness centrality for nodes and edges.

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HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks

Cited by 15 publications

References 48 publications

Beyond non-backtracking: non-cycling network centrality measures

Beyond non-backtracking: non-cycling network centrality measures

Causal motifs and existence of endogenous cascades in directed networks with application to company defaults

A Path-based Approach to Analyzing the Global Liner Shipping Network

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