Uncovering nodes that spread information between communities in social networks

Abstract

From many datasets gathered in online social networks, well defined community structures have been observed. A large number of users participate in these networks and the size of the resulting graphs poses computational challenges. There is a particular demand in identifying the nodes responsible for information flow between communities; for example, in temporal Twitter networks edges between communities play a key role in propagating spikes of activity when the connectivity between communities is sparse and few edges exist between different clusters of nodes. The new algorithm proposed here is aimed at revealing these key connections by measuring a node's vicinity to nodes of another community. We look at the nodes which have edges in more than one community and the locality of nodes around them which influence the information received and broadcasted to them. The method relies on independent random walks of a chosen fixed number of steps, originating from nodes with edges in more than one community. For the large networks that we have in mind, existing measures such as betweenness centrality are difficult to compute, even with recent methods that approximate the large number of operations required. We therefore design an algorithm that scales up to the demand of current big data requirements and has the ability to harness parallel processing capabilities. The new algorithm is illustrated on synthetic data, where results can be judged carefully, and also on a real, large scale Twitter activity data, where new insights can be gained.

Asymmetry through time dependency

“…From an application of MATLAB's randperm function, we took timepoints in the order 5,16,1,11,9,14,13,3,7,10,4,2,6,12,8,15. In this case, the cascade effect is likely to be reduced and it is of interest to quantify how much asymmetry remains.…”

“…Fiedler reordering has reduced the two-sum, but has not revealed the inherent asymmetry. In the lower right picture, we show the "in minus out" reordering, which solves (6). In this case, we see that nonzeros are moved up and to the right.…”

“…However, even after simplifying down to the level of nodes and edges, there is typically too much information for us to digest, and we must rely on tools that further reduce the dimension of the system so that we can summarize and visualize key properties. The need to reveal hidden structure and substructure within a complex network has motivated a plethora of quantitative tools aimed at, for example, discovering significant nodes or edges, and topological features such as well-connected communities, bipartite structures, bottlenecks, motifs, hubs and authorities [2][3][4][5][6][7][8][9]. In this work we focus on the idea of quantifying and visualizing the level of asymmetry in a network, and in particular, studying asymmetry caused by the arrow of time in a dynamic network sequence.…”

On community structure in complex networks: challenges and opportunities

“…Different from the above method, Mantzaris [88] proposed the Boundary Vicinity Algorithm BVA. Boundary Vicinity Algorithm (BVA): This strategy ranks nodes according to their vicinity to bridge nodes (boundary nodes) of each community.…”

An LSTM Model for Predicting Cross-Platform Bursts of Social Media Activity

“…It is intriguing how "intense activity followed by longer periods of inactivity" can manifest in social coding platforms [10] from complex timelines of work interspersed with communication about version control. These changes are non-linear in that they do not follow an accumulated trend from previous time points and also manifest themselves within subcomponents of the network such as the "boundary-nodes" (community spanners) [11]. Although the non-linearity poses a direct challenge to accurately predicting their occurrences, their impact affect our societies at large.…”