Abstract
SummaryInfluential node identification plays a significant role in understanding network structure and functions. Here we propose a general method for detecting influential nodes in a graph-traversal framework. We evaluate the influence of each node by constructing a breadth-first search (BFS) tree in which the target node is the root node. From the BFS tree, we generate a curve in which the x axis is the level number and the y axis is the cumulative scores of all nodes visited so far. We use the area under the curve value as the final influence score of the target node. Experimental results on various networks across different domains demonstrate that our method can be significantly superior to widely used centrality measures on the task of influential node detection.
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