Abstract:
The high connectivity of the modern day world has lead to the easy diffusion of harmful information content like rumors, computer viruses, and contagions like diseases. This is detrimental to human societies
in terms of compromised security, monetary loss and life threats. Therefore, to mitigate such damages,
identifying and timely quarantining the sources of such diffusion processes is highly critical. Despite the
difficulty and challenges of the problem, researchers have extensively studied source identification in
complex networks over the past decade. Several approaches have been proposed, but limited attention
has been given to the classical graph centrality measures. Moreover, researchers have generally advised
against employing these measures for identifying infection sources. Motivated by the same, in this paper,
we revisit these measures in context to source identification and raise the question: “Are classical graph
centrality measures really not good enough?”, and perform an extensive experimental journey. We pick
five such measures, viz., betweenness, closeness, degree, eigenvector and eccentricity, and conduct an indepth analysis of their effectiveness in source detection. Our extensive results show that, contrary to the
popular belief, a combination of eccentricity and closeness (EC+CC) generally performs better than several state-of-the-art source identification techniques, with higher accuracy and lower average hop error.
We also analyze the impact of infection size on source identification and observe that EC+CC is generally
highly scalable and stable as well. We also understand that as the infection size increases, the detection
accuracy decreases, irrespective of the technique used. However, when we examine the effect of graph
density, we observe that as the graph density increases, the detection accuracy increases as well, with
EC+CC again outperforming state-of-the-art.
