An efficient procedure for mining egocentric temporal motifs

Abstract

Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as temporal motifs, is a challenging task. In this work, we present an efficient technique for extracting temporal motifs in temporal networks. Our method is based on the novel notion of egocentric temporal neighborhoods, namely multi-layer structures centered on an ego node. Each temporal layer of the structure consists of the first-order neighborhood of the ego node, and corresponding nodes in sequential layers are connected by an edge. The strength of this approach lies in the possibility of encoding these structures into a unique bit vector, thus bypassing the problem of graph isomorphism in searching for temporal motifs. This allows our algorithm to mine substantially larger motifs with respect to alternative approaches. Furthermore, by bringing the focus on the temporal dynamics of the interactions of a specific node, our model allows to mine temporal motifs which are visibly interpretable. Experiments on a number of complex networks of social interactions confirm the advantage of the proposed approach over alternative non-egocentric solutions. The egocentric procedure is indeed more efficient in revealing similarities and discrepancies among different social environments, independently of the different technologies used to collect data, which instead affect standard non-egocentric measures.

Modeling framework unifying contact and social networks

“…e. Egocentric Temporal Network (ETN) [26,27]: An ETN (see Fig. 6) corresponds to a representation of the diversity of the interaction partners of a given node (the ego, in red in Fig.…”

“…We do not intend to answer the question of which list of observables would fully characterize a social system represented as a temporal network, as this question is not fully answered even for static network representations [25]. However we extend the set of commonly used observables: we consider the distributions of the node activity duration and interduration, and of the duration of newly established edges, as well as structural patterns such as the size of connected components in the instantaneous graph of interactions, and spatio-temporal patterns like Egocentric Temporal Networks (ETN) [26], which have recently been shown to be useful building blocks to decompose a temporal network [27].…”

Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets

“…In the last decades, graphs has been involved in many domains such as: molecular biology [7], face-to-face interactions [8], [9], contact tracing [10] and for social networks [11], [12]. Graph structures are capable of informing powerful modeling in deep learning, due to their non-euclidean domain.…”

Neighbourhood matching creates realistic surrogate temporal networks

“…In this work, we propose a method able to generate high temporal resolution surrogate networks that are able to match real-networks in terms of a wide range of topological and dynamic measures. Our generative algorithm is based on the idea of the egocentric temporal neighborhood 37 E {t−k,...,t} n for node n at time t, including a small number k of prior time steps. Here we assume that the network is represented in discrete time with each time step corresponding to a static graph, also referred to as a 'layer' of the network.…”