Identifying Influential Links for Event Propagation on Twitter: A Network of Networks Approach

Chen, Pin-Yu; Tu, Chun‐Chen; Ting, Pei‐Yih; Lo, Ya-Yun; Koutra, Danai; Hero, Alfred O.

doi:10.1109/tsipn.2018.2866318

Cited by 5 publications

(1 citation statement)

References 41 publications

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“…Before continuing, note that there exist some other network models of higher-order data. These include: multiplex, multimodal, multilevel, and interdependent networks [25,16,7,14,9,23], which are sometimes used interchangeably and sometimes also referred to as "networks of networks"; hierarchical networks [8]; higher-order networks [31]; hypergraphs [4]; and simplicial complexes [17]. However, these all model different types of data compared to NoNs as we define them, so we cannot consider these other network types in our study.…”

Section: Introductionmentioning

confidence: 99%

Modeling multi-scale data via a network of networks

Gu¹,

Jiang²,

Guzzi³

et al. 2021

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Prediction of node and graph labels are prominent network science tasks. Data analyzed in these tasks are sometimes related: entities represented by nodes in a higher-level (higher-scale) network can themselves be modeled as networks at a lower level. We argue that systems involving such entities should be integrated with a "network of networks" (NoN) representation. Then, we ask whether entity label prediction using multi-level NoN data via our proposed approaches is more accurate than using each of single-level node and graph data alone, i.e., than traditional node label prediction on the higherlevel network and graph label prediction on the lower-level networks. To obtain data, we develop the first synthetic NoN generator and construct a real biological NoN. We evaluate accuracy of considered approaches when predicting artificial labels from the synthetic NoNs and proteins' functions from the biological NoN. For the synthetic NoNs, our NoN approaches outperform or are as good as node-and network-level ones depending on the NoN properties. For the biological NoN, our NoN approaches outperform the single-level approaches for just under half of the protein functions, and for 30% of the functions, only our NoN approaches make meaningful predictions, while node-and network-level ones achieve random accuracy. So, NoN-based data integration is important.

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