A Formal Treatment of Generalized Preferential Attachment and its Empirical Validation

Generalized preferential attachment is defined as the tendency of a vertex to acquire new links in the future with respect to a particular vertex property. Understanding which properties influence link acquisition tendency (LAT) gives us a predictive power to estimate the future growth of network and insight about the actual dynamics governing the complex networks. In this study, we explore the effect of age and degree on LAT by analyzing data collected from a new complex-network growth dataset. We found that LAT and degree of a vertex are linearly correlated in accordance with previous studies. Interestingly, the relation between LAT and age of a vertex is found to be in conflict with the known models of network growth. We identified three different periods in the network's lifetime where the relation between age and LAT is strongly positive, almost stationary and negative correspondingly

Structural Deep Embedding for Hyper-Networks

Network embedding has recently attracted lots of attentions in data mining. Existing network embedding methods mainly focus on networks with pairwise relationships. In real world, however, the relationships among data points could go beyond pairwise, i.e., three or more objects are involved in each relationship represented by a hyperedge, thus forming hyper-networks. These hyper-networks pose great challenges to existing network embedding methods when the hyperedges are indecomposable, that is to say, any subset of nodes in a hyperedge cannot form another hyperedge. These indecomposable hyperedges are especially common in heterogeneous networks. In this paper, we propose a novel Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with indecomposable hyperedges. More specifically, we theoretically prove that any linear similarity metric in embedding space commonly used in existing methods cannot maintain the indecomposibility property in hyper-networks, and thus propose a new deep model to realize a non-linear tuplewise similarity function while preserving both local and global proximities in the formed embedding space. We conduct extensive experiments on four different types of hyper-networks, including a GPS network, an online social network, a drug network and a semantic network. The empirical results demonstrate that our method can significantly and consistently outperform the state-of-the-art algorithms.Comment: Accepted by AAAI 1

The Formation of Risk Sharing Networks.

This paper examines the formation of risk sharing networks in the rural Philippines. We find that geographic proximity–possibly correlated with kinship–is a major determinant of mutual insurance links among villagers. Age and wealth differences also play an important role. In contrast, income correlation and differences in occupation are not determinants of network links. Reported network links have a strong effect on subsequent gifts and loans. Gifts between network partners are found to respond to shocks and to differences in health status. From this we conclude that intra-village mutual insurance links are largely determined by social and geographical proximity and are only weakly the result of purposeful diversification of income risk. The paper also makes a methodological contribution to the estimation of dyadic models.partage du risque; modèle dyadique; dyadic model; Philippines; Network; risk-sharing; Réseau;