Analytics for directed contact networks

Cybenko, George; Huntsman, Steve

doi:10.1007/s41109-019-0209-1

Cited by 6 publications

(6 citation statements)

References 40 publications

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“…And the total of our channels can be increased. For example, Sherifdeen Lawa [43] introduced microfrontend and it could be used to deploy the microservice faster and more fexible [14].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Covert-Aware Anonymous Communication Network for Social Communication

Cheng

Zhao³

et al. 2022

Security and Communication Networks

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To effectively protect the communication content and communication behavior of social networks, anonymous communication technologies are widely used. However, the anonymous communication networks represented by Tor and I2P lack the covertness of the control plane design, which leads to important user behavioral characteristics in the process of accessing anonymous communication networks. Therefore, network monitors can analyze users' communication behavior by tracking these characteristics. In this paper, the concept of covert measurement is proposed. On this basis, a software-defined anonymous communication network architecture is presented, which also considers the covertness of the anonymous communication network control plane and the data plane. According to the theoretical analysis and experimental results, the anonymous communication network architecture proposed in this paper has better anonymity and usability than traditional anonymous communication networks, such as Tor.

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“…And the total of our channels can be increased. For example, Sherifdeen Lawa [43] introduced microfrontend and it could be used to deploy the microservice faster and more fexible [14].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A Covert-Aware Anonymous Communication Network for Social Communication

Cheng

Zhao³

et al. 2022

Security and Communication Networks

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“…The event sequences are bursty when considering the time span of the dataset and the dynamics of hadith collection and narration. Consequently, this network exhibits strong parallels with a directed social contact network (Cybenko and Huntsman 2019;Holme 2005) but with only partial temporal information available. Trophic analysis will render a temporal order, that is strongly congruent to a hierarchical order We then leverage the sequential structure to allow us to interpolate temporal positions, as we illustrate later.…”

Section: Fig 2 Hadith Social Networkmentioning

confidence: 99%

“…Early work in this area was done by Kempe et al where they define a typical inference problem in the temporal network space (Kempe et al 2002):the problem of inferring if a time respected path exists between certain sets of nodes. Directed contact networks is another close analogue (Cybenko and Huntsman 2019) in the literature. They consist of a directed network with a temporal element and the constituent element is known as a contact.…”

Section: Background and Related Workmentioning

confidence: 99%

Trophic analysis of a historical network reveals temporal information

Shuaib

Syed²,

Halawi³

et al. 2022

Appl Netw Sci

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Trophic analysis exposes the underlying hierarchies present in large complex systems. This allows one to use data to diagnose the sources, propagation paths, and basins of influence of shocks or information among variables or agents, which may be utilised to analyse dynamics in social, economic and historical data sets. Often, the analysis of static networks provides an aggregated picture of a dynamical process and explicit temporal information is typically missing or incomplete. Yet, for many networks, particularly historical ones, temporal information is often implicit, for example in the direction of edges in a network. In this paper, we show that the application of trophic analysis allows one to use the network structure to infer temporal information. We demonstrate this on a sociohistorical network derived from the study of hadith, which are narratives about the Prophet Muhammad’s actions and sayings that cite the people that transmitted the narratives from one generation to the next before they were systematically written down. We corroborate the results of the trophic analysis with a partially specified time labelling of a subset of the transmitters. The results correlate in a manner consistent with an observed history of information transmission flowing through the network. Thus, we show that one may reconstruct a temporal structure for a complex network in which information diffuses from one agent to another via social links and thus allows for the reconstruction of an event based temporal network from an aggregated static snapshot. Our paper demonstrates the utility of trophic analysis in revealing novel information from hierarchical structure, thus showing its potential for probing complex systems, particularly those with an inherent asymmetry.

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“…We analyze three temporal networks (Holme 2015; Masuda and Lambiotte 2020): MathOverflow, an email network, and activity on a Facebook group. These are all directed contact networks (DCNs) in the sense of Cybenko and Huntsman (2019): i.e., each network event ("contact") is specified by a source, a target, and a time. Formally, a DCN with (spatial) vertex set V ≡ [n] ≡ {1, .…”

Section: Applications To Window-aggregated Temporal Networkmentioning

confidence: 99%

Path homologies of motifs and temporal network representations

Chowdhury

Huntsman²,

Yutin

2022

Appl Netw Sci

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Path homology is a powerful method for attaching algebraic invariants to digraphs. While there have been growing theoretical developments on the algebro-topological framework surrounding path homology, bona fide applications to the study of complex networks have remained stagnant. We address this gap by presenting an algorithm for path homology that combines efficient pruning and indexing techniques and using it to topologically analyze a variety of real-world complex temporal networks. A crucial step in our analysis is the complete characterization of path homologies of certain families of small digraphs that appear as subgraphs in these complex networks. These families include all digraphs, directed acyclic graphs, and undirected graphs up to certain numbers of vertices, as well as some specially constructed cases. Using information from this analysis, we identify small digraphs contributing to path homology in dimension two for three temporal networks in an aggregated representation and relate these digraphs to network behavior. We then investigate alternative temporal network representations and identify complementary subgraphs as well as behavior that is preserved across representations. We conclude that path homology provides insight into temporal network structure, and in turn, emergent structures in temporal networks provide us with new subgraphs having interesting path homology.

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Analytics for directed contact networks

Cited by 6 publications

References 40 publications

A Covert-Aware Anonymous Communication Network for Social Communication

A Covert-Aware Anonymous Communication Network for Social Communication

Trophic analysis of a historical network reveals temporal information

Path homologies of motifs and temporal network representations

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