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.
The dynamic nature of web content has been of interest because of its implications for search and retrieval @cite_21 @cite_12 . In particular, changes in content and link structure can be used to find trending content. @cite_10 used bursts in appearance of connections between entities in text to detect events. Although they related network structure to properties of content, the network was generated from entities within the content itself. @cite_18 analyzed a joint model of network and topic evolution to track and predict topic popularity, but did not explicitly examine network structure. Two other studies used aggregate volume of interactions in social media to infer the evolution of content, but did not explicitly examine the networks' structure. @cite_13 used the volume of interactions between nodes in social media, along with other variables, to identify attention gathering items early on in their lifecycle. In a non-Web context, @cite_25 showed that the communication volume among stock traders correlated with synchrony in their trading behavior and correspondingly with profits.
@cite_18 @cite_10 @cite_21 @cite_13 @cite_25 @cite_12
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User generated information in online communities has been characterized with the mixture of a text stream and a network structure both changing over time. A good example is a web-blogging community with the daily blog posts and a social network of bloggers. An important task of analyzing an online community is to observe and track the popular events, or topics that evolve over time in the community. Existing approaches usually focus on either the burstiness of topics or the evolution of networks, but ignoring the interplay between textual topics and network structures. In this paper, we formally define the problem of popular event tracking in online communities (PET), focusing on the interplay between texts and networks. We propose a novel statistical method that models the the popularity of events over time, taking into consideration the burstiness of user interest, information diffusion on the network structure, and the evolution of textual topics. Specifically, a Gibbs Random Field is defined to model the influence of historic status and the dependency relationships in the graph; thereafter a topic model generates the words in text content of the event, regularized by the Gibbs Random Field. We prove that two classic models in information diffusion and text burstiness are special cases of our model under certain situations. Empirical experiments with two different communities and datasets (i.e., Twitter and DBLP) show that our approach is effective and outperforms existing approaches. This paper studies the problem of dynamic relationship and event discovery. A large body of previous work on relation extraction focuses on discovering predefined and static relationships between entities. In contrast, we aim to identify temporally defined (e.g., co-bursting) relationships that are not predefined by an existing schema, and we identify the underlying time constrained events that lead to these relationships. The key challenges in identifying such events include discovering and verifying dynamic connections among entities, and consolidating binary dynamic connections into events consisting of a set of entities that are connected at a given time period. We formalize this problem and introduce an efficient end-to-end pipeline as a solution. In particular, we introduce two formal notions, global temporal constraint cluster and local temporal constraint cluster, for detecting dynamic events. We further design efficient algorithms for discovering such events from a large graph of dynamic relationships. Finally, detailed experiments on real data show the effectiveness of our proposed solution. We seek to gain improved insight into how Web search engines shouldcope with the evolving Web, in an attempt to provide users with themost up-to-date results possible. For this purpose we collectedweekly snapshots of some 150 Web sites over the course of one year,and measured the evolution of content and link structure. Our measurements focus on aspects of potential interest to search engine designers: the evolution of link structure over time, the rate ofcreation of new pages and new distinct content on the Web, and the rate of change of the content of existing pages under search-centric measures of degree of change.Our findings indicate a rapid turnover rate of Web pages, i.e.,high rates of birth and death, coupled with an even higher rate ofturnover in the hyperlinks that connect them. For pages that persistover time we found that, perhaps surprisingly, the degree of contentshift as measured using TF.IDF cosine distance does not appear to beconsistently correlated with the frequency of contentupdating. Despite this apparent non-correlation, the rate of content shift of a given page is likely to remain consistent over time. That is, pages that change a great deal in one week will likely change by a similarly large degree in the following week. Conversely, pages that experience little change will continue to experience little change. We conclude the paper with a discussion of the potential implications ofour results for the design of effective Web search engines. The Web is a dynamic, ever changing collection of information. This paper explores changes in Web content by analyzing a crawl of 55,000 Web pages, selected to represent different user visitation patterns. Although change over long intervals has been explored on random (and potentially unvisited) samples of Web pages, little is known about the nature of finer grained changes to pages that are actively consumed by users, such as those in our sample. We describe algorithms, analyses, and models for characterizing changes in Web content, focusing on both time (by using hourly and sub-hourly crawls) and structure (by looking at page-, DOM-, and term-level changes). Change rates are higher in our behavior-based sample than found in previous work on randomly sampled pages, with a large portion of pages changing more than hourly. Detailed content and structure analyses identify stable and dynamic content within each page. The understanding of Web change we develop in this paper has implications for tools designed to help people interact with dynamic Web content, such as search engines, advertising, and Web browsers.