As individuals communicate, their exchanges form a dynamic network. We demonstrate, using time series analysis of communication in three online settings, that network structure alone can be highly revealing of the diversity and novelty of the information being communicated. Our approach uses both standard and novel network metrics to characterize how unexpected a network configuration is, and to capture a network's ability to conduct information. We find that networks with a higher conductance in link structure exhibit higher information entropy, while unexpected network configurations can be tied to information novelty. We use a simulation model to explain the observed correspondence between the evolution of a network's structure and the information it carries.
Prior work that has examined time-evolving network structure explicitly has shown that changes in a network's structure can be reflective of events and trends. In the Graphscope project @cite_8 , changes in community structure of email communication networks were tied to events within a company. @cite_4 showed that breaks in supreme court citation patterns corresponded to changes in the court's ideology. @cite_14 correlated time series of networks of traders trading in commodity futures contracts, with financial variables related to the trades, such as returns, volatility and duration. However, the notion of information entering into the market was implicit, in the sense that the prices and quantities of contracts traded reflected the information the traders held about the future value of the contract. In contrast, in this paper we explicitly analyze the information that is being directly communicated with each activation of an edge.
@cite_14 @cite_4 @cite_8
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We use network analysis to quantify the flow of information through financial markets. Using unique ultra high frequency data, we compute network and financial variables for transactions that occured during August 2008 in the nearby E-mini S&P 500 futures contract ‐ the cornerstone of price discovery for the S&P 500 Index. We find that network variables presage the information represented by financial variables. Most notably, we find that network variables strongly Granger-case intertrade duration and trading volume, suggesting that network metrics serve as primitive measures of information flow. Finally, we find that the dynamics of returns and volatility are rooted in the network mechanics of the information arrival process ‐ as evidenced both in our data and the results of an agent-based simulation model. In this paper we examine a number of methods for probing and understanding the large-scale structure of networks that evolve over time. We focus in particular on citation networks, networks of references between documents such as papers, patents, or court cases. We describe three different methods of analysis, one based on an expectation-maximization algorithm, one based on modularity optimization, and one based on eigenvector centrality. Using the network of citations between opinions of the United States Supreme Court as an example, we demonstrate how each of these methods can reveal significant structural divisions in the network and how, ultimately, the combination of all three can help us develop a coherent overall picture of the network's shape. How can we find communities in dynamic networks of socialinteractions, such as who calls whom, who emails whom, or who sells to whom? How can we spot discontinuity time-points in such streams of graphs, in an on-line, any-time fashion? We propose GraphScope, that addresses both problems, using information theoretic principles. Contrary to the majority of earlier methods, it needs no user-defined parameters. Moreover, it is designed to operate on large graphs, in a streaming fashion. We demonstrate the efficiency and effectiveness of our GraphScope on real datasets from several diverse domains. In all cases it produces meaningful time-evolving patterns that agree with human intuition.