Nodes in networks that exhibit high connectivity, also called ``hubs'', play a critical role in determining the structural and functional properties of networked systems. However, there is no clear definition of what constitutes a hub node in a network, and the classification of network hubs in existing work has either been purely qualitative or relies on ad hoc criteria for thresholding continuous data that do not generalize well to networks with certain degree sequences. Here we develop a set of efficient nonparametric methods that classify hub nodes in directed networks using the Minimum Description Length principle, effectively providing a clear and principled definition for network hubs. We adapt our methods to both unweighted and weighted networks and demonstrate them in a range of example applications using real and synthetic network data.
Terrorism instills fear in the minds of people and takes away the freedom of individuals to act as they will. Terrorism has turned out to be an international menace today. Here, we study the terrorist attack incidents which occurred in the last half-century across the globe from the open source, Global Terrorism Database, and develop a view on their spatio-temporal dynamics. We construct a complex network of global terrorism and study its growth dynamics, along with the statistical properties of the anti-social network, which are quite intriguing. Normally, each nation pursues its own vision o…
In this work we study international migrations of researchers, scientists, and academics from a complex-network perspective to identify the central countries involved in the migration phenomenon. We define the scientific migration network (SMN) as temporal directed weighted network where nodes are world countries, links account for the number of scientists moving from one country to another, and timestamps represent years (from 2000 to 2016). 2.8 millions ORCID public profiles are utilized as data source. We then characterize hubs and authorities of the SMN employing the well-know weighted hyp…
Understanding course enrollment patterns is valuable to predict upcoming demands for future courses, and to provide student with realistic courses to pursue given their current backgrounds. This study uses undergraduate student enrollment data to form networks of courses where connections are based on student co-enrollments. The course networks generated in this paper are based on eight years of undergraduate course enrollment data from a large metropolitan university. The networks are analyzed to identify "hub" courses often taken with many other courses. Two notions of hubs are considered: o…
We investigate to what extent the degree sequence of a directed network constrains the number of driver nodes. We develop a pair of algorithms that take a directed degree sequence as input and aim to output a network with the maximum or minimum number of driver nodes. We find an upper bound for the maximum and a lower bound for the minimum, and show that the algorithms achieve these bounds for all real and model networks, with few exceptions characterized by tiny system size and heterogeneous degree distributions. Applying these algorithms to a broad range of real networks, we show the gap bet…
Many online social networks allow directed edges: Alice can unilaterally add an "edge" to Bob, typically indicating interest in Bob or Bob's content, without Bob's permission or reciprocation. In directed social networks we observe the rise of two distinctive classes of users: celebrities who accrue unreciprocated incoming links, and follow spammers, who generate unreciprocated outgoing links. Identifying users in these two classes is important for abuse detection, user and content ranking, privacy choices, and other social network features. In this paper we develop SCRank, an iterative algori…
University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；IBM UK（IBM英国公司）；Cardiff University（卡迪夫大学）
Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world. However, most existing work in this area focus on examining static undirected networks without considering directed edges or temporality. In this paper, we propose a new methodology that utilizes feature representation for network classification based on the temporal motif distribution of the network and a null model for comparing against random graphs. Experimental results show that our method improves accu…
GravityGraphSAGE: Link Prediction in Directed Attributed Graphs
Department of Excellence L’EMbeDS, Sant’Anna School of Advanced Studies（卓越部门L’EMbeDS，圣安娜高级研究学校）；Department of Computer Science, University of Pisa（比萨大学计算机科学系）；Department of Statistics and Huck Institutes of the Life Sciences, The Pennsylvania State University（统计学与生命科学学院，宾夕法尼亚州立大学）；Class of Science, Scuola Normale Superiore（科学班级，正规大学）；DTU Technical University of Denmark（丹麦技术大学）
