The Atlas of Chinese World Wide Web Ecosystem Shaped by the Collective Attention Flows

Abstract

The web can be regarded as an ecosystem of digital resources connected and shaped by collective successive behaviors of users. Knowing how people allocate limited attention on different resources is of great importance. To answer this, we embed the most popular Chinese web sites into a high dimensional Euclidean space based on the open flow network model of a large number of Chinese users’ collective attention flows, which both considers the connection topology of hyperlinks between the sites and the collective behaviors of the users. With these tools, we rank the web sites and compare their centralities based on flow distances with other metrics. We also study the patterns of attention flow allocation, and find that a large number of web sites concentrate on the central area of the embedding space, and only a small fraction of web sites disperse in the periphery. The entire embedding space can be separated into 3 regions(core, interim, and periphery). The sites in the core (1%) occupy a majority of the attention flows (40%), and the sites (34%) in the interim attract 40%, whereas other sites (65%) only take 20% flows. What’s more, we clustered the web sites into 4 groups according to their positions in the space, and found that similar web sites in contents and topics are grouped together. In short, by incorporating the open flow network model, we can clearly see how collective attention allocates and flows on different web sites, and how web sites connected each other.

The Hidden Flow Structure and Metric Space of Network Embedding Algorithms Based on Random Walks

“…Complex networks, as high-level abstractions of complex system, have been widely applied in different areas, such as biology, sociology, economics and technology [1][2][3][4][5][6] . Recent progress has revealed a hidden geometric structure in networks 7,8 that not only deepens our understanding of the multiscale nature and intrinsic heterogeneity of networks but also provides a useful tool to unravel the regularity of some dynamic processes on networks 7,[9][10][11][12][13][14] . At the same time, researchers in the machine learning community have developed several techniques to embed a whole network in a high-dimensional space [15][16][17][18][19][20] such that the vectors of each node can be used as abstract features feeding on neural networks to perform tasks.…”