ABSTRACT

Transmission Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 4.5 Temporal Degrees and Hubs: Ranking and Predictio . . . . . . . . . . . . . . . . 112 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

4.1 Introduction Since the publication of Moreno and Jennings’ sociometry book in 1934 [1], network (graph) theory has become one of the most powerful tools to characterize social interactions and population dynamics [2, 3, 4]. The discoveries of small-world [5] and scale-free networks [6] in the late of 1990s focused world-wide attention on complex networks and network science. We have witnessed fruitful and exciting advances to understand the hidden patterns behind complex connectivity features and characteristics of diverse large-scale networking systems. Such natural and/or man-made examples range from the Internet, the World Wide Web, biological brain networks, proteinto-protein interaction networks, power grids, and wireless communication networks, to categories of social, economic, and financial networks at different levels of human society. Extensive efforts and elegant attempts have been devoted to answering a fundamental question: how does the fascinating complex topological features affect or determine the collective behaviors and performance of the corresponding complex networked system [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21]. This widely accepted key question still remains open in the fast-developing field of network science, especially in the newly focused situation that temporal information as an explicit element defines the edges of such a so-called temporal network [22] when they are active.