When is a Network a Network?

Scholtes, Ingo

doi:10.1145/3097983.3098145

Cited by 56 publications

(24 citation statements)

References 24 publications

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“…If higher-order Markov chains are required, we should assume a larger state-space and use these states as the nodes of our individual mobility networks. Recently proposed methods on optimal order selection of sequential data [28,39] can be directly applied at this step.…”

Section: Mobility Information Networkmentioning

confidence: 99%

Quantifying Privacy Loss of Human Mobility Graph Topology

Manousakas

Mascolo

Beresford

et al. 2018

Proceedings on Privacy Enhancing Technologies

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Human mobility is often represented as a mobility network, or graph, with nodes representing places of significance which an individual visits, such as their home, work, places of social amenity, etc., and edge weights corresponding to probability estimates of movements between these places. Previous research has shown that individuals can be identified by a small number of geolocated nodes in their mobility network, rendering mobility trace anonymization a hard task. In this paper we build on prior work and demonstrate that even when all location and timestamp information is removed from nodes, the graph topology of an individual mobility network itself is often uniquely identifying. Further, we observe that a mobility network is often unique, even when only a small number of the most popular nodes and edges are considered. We evaluate our approach using a large dataset of cell-tower location traces from 1 500 smartphone handsets with a mean duration of 430 days. We process the data to derive the top−N places visited by the device in the trace, and find that 93% of traces have a unique top−10 mobility network, and all traces are unique when considering top−15 mobility networks. Since mobility patterns, and therefore mobility networks for an individual, vary over time, we use graph kernel distance functions, to determine whether two mobility networks, taken at different points in time, represent the same individual. We then show that our distance metrics, while imperfect predictors, perform significantly better than a random strategy and therefore our approach represents a significant loss in privacy.

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Section: Mobility Information Networkmentioning

confidence: 99%

Quantifying Privacy Loss of Human Mobility Graph Topology

Manousakas

Mascolo

Beresford

et al. 2018

Proceedings on Privacy Enhancing Technologies

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show abstract

“…work), and then returns home with a high probability (Salnikov et al 2016). Consequently, the naive application of (static) network paradigm in modelling dynamic complex systems might lead to wrong conclusions (Rosvall et al 2014;Scholtes et al 2014;Scholtes 2017). One way to address this issue is through the extension to a multi-layer networks representation.…”

Section: Traffic On Complex Networkmentioning

confidence: 99%

“…To evaluate the obtained results, a comparison between the proposed NiN model and a traditional complex network diffusion process on a single-layer network (SLN) was made. Since the traffic flows are edge properties, the initial topology of the road network has been converted into a higher-order network of order two (Rosvall et al 2014;Scholtes 2017;Lambiotte et al 2018), i.e. intersections were turned into edges and road sections into vertices (Porta et al 2006).…”

Section: Single-layer Network Approachmentioning

confidence: 99%

Estimation of traffic flow changes using networks in networks approaches

Hackl

Adey

2019

Appl Netw Sci

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Understanding traffic flow in urban areas has great importance and implications from an economic, social and environmental point of view. For this reason, numerous disciplines are working on this topic. Although complex network theory made their appearance in transportation research through empirical measures, the relationships between dynamic traffic patterns and the underlying transportation network structures have scarcely been investigated so far. In this work, a novel Networks in Networks (NiN) approach is presented to study changes in traffic flows, caused by topological changes in the transportation network. The NiN structure is a special type of multi-layer network in which vertices are networks themselves. This embedded network structure makes it possible to encode multiple pieces of information such as topology, paths, and origin-destination information, within one consistent graph structure. Since each vertex is an independent network in itself, it is possible to implement multiple diffusion processes with different physical meanings. In this way, it is possible to estimate how the travellers' paths will change and to determine the cascading effect in the network. Using the Sioux Falls benchmark network and a real-world road network in Switzerland, it is shown that NiN models capture both topological and spatial-temporal patterns in a simple representation, resulting in a better traffic flow approximation than single-layer network models.

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“…Deciding the order of the model is not trivial as specific patterns can be revealed only on a specific subset of memory models. To solve this problem, Scholtes et al [16] introduced a multilayer memory network, composed of multiple memory networks of different order hierarchically connected between them (e.g., each node in the 2nd-order layer v ij is connected with all nodes in the 3rd-order layer whose path v k lm contains the leg Time often plays an important role when networks are concerned, because networks often represent dynamical systems. However, in Table 1 we have only listed distinct data models explicitly providing time annotations.…”

Section: Time and Topologymentioning

confidence: 99%

Foundations of Temporal Text Networks

Vega

Magnani

2018

Appl Netw Sci

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Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.

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When is a Network a Network?

Cited by 56 publications

References 24 publications

Quantifying Privacy Loss of Human Mobility Graph Topology

Quantifying Privacy Loss of Human Mobility Graph Topology

Estimation of traffic flow changes using networks in networks approaches

Foundations of Temporal Text Networks

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