Temporal walk based centrality metric for graph streams

Abstract

A plethora of centrality measures or rankings have been proposed to account for the importance of the nodes of a network. In the seminal study of Boldi and Vigna (2014), the comparative evaluation of centrality measures was termed a difficult, arduous task. In networks with fast dynamics, such as the Twitter mention or retweet graphs, predicting emerging centrality is even more challenging.Our main result is a new, temporal walk based dynamic centrality measure that models temporal information propagation by considering the order of edge creation. Dynamic centrality measures have already started to emerge in publications; however, their empirical evaluation is limited. One of our main contributions is creating a quantitative experiment to assess temporal centrality metrics. In this experiment, our new measure outperforms graph snapshot based static and other recently proposed dynamic centrality measures in assigning the highest time-aware centrality to the actually relevant nodes of the network. Additional experiments over different data sets show that our method perform well for detecting concept drift in the process that generates the graphs.

weg2vec: Event embedding for temporal networks

“…It also applies random walks to generate environments possibly using two strategies, the Temporal Walk algorithm and the Temporal Neighbourhood algorithm. In the Temporal Walk algorithm [41] a temporal path based centrality metric is used to capture similarity between nodes by projecting nodes on the same temporal path close to each other in the embedding. In the Temporal Neighbourhood algorithm [42], node similarity is inferred via a fingerprinting method, which projects nodes with similar neighbourhoods close to each other.…”

Node embeddings in dynamic graphs

“…The concept was perhaps introduced in (Moody 2002) for analyzing diffusion in networks. In another terminology, temporal walks were used to construct time-aware centrality metrics in (Rozenshtein and Gionis 2016;Béres et al 2018).…”

“…Then, at a time instance, we compute the list of nodes closest to selected ones in the embedding, and compare these lists against the similarity ground truth. We analyze node embedding methods for similarity search over the Twitter tennis tournament collections of (Béres et al 2018). For the quantitative analysis, we use the annotation of the nodes for the accounts of the tennis players that participate in a game on a given day.…”

“…In (Béres et al 2018), we compiled two separate tweet collections: RG17 for Roland-Garros, the French Open Tennis Tournament, and UO17 for US Open, the United States Open Tennis Championships, which we use in our first experiment. We use the mention graphs extracted from the last 15 and 14 days of RG17 and UO17, respectively.…”

“…• We design two algorithms that can update their node representations quickly in large graph streams, and outperform the baselines among others in link prediction tasks. • We design a quantitative experiment for accessing the quality of temporal node embeddings based on the Twitter tennis tournament mention graphs of (Béres et al 2018), which include temporally changing node labels. In a supervised experiment, Fig.…”