This is the first work investigating community structure and interaction dynamics through the lens of quotes in online discussion forums. We examine four forums of different size, language, and topic. Quote usage, which is surprisingly consistent over time and users, appears to have an important role in aiding intra-thread navigation, and uncovers a hidden “social” structure in communities otherwise lacking all trappings (from friends and followers to reputations) of today’s social networks.
A related research line of considerable practical interest involves inferring social networks from the actual observed interactions (such as exchanged messages or co-presence at events) @cite_9 . Platforms analyzed in the literature include academic citation networks @cite_7 , online college communities @cite_15 , email @cite_2 , and phone call logs @cite_3 ; yet quotes in online forums have never been investigated to date.
@cite_7 @cite_9 @cite_3 @cite_2 @cite_15
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How do real graphs evolve over time? What are "normal" growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include heavy tails for in- and out-degree distributions, communities, small-world phenomena, and others. However, given the lack of information about network evolution over long periods, it has been hard to convert these findings into statements about trends over time.Here we study a wide range of real graphs, and we observe some surprising phenomena. First, most of these graphs densify over time, with the number of edges growing super-linearly in the number of nodes. Second, the average distance between nodes often shrinks over time, in contrast to the conventional wisdom that such distance parameters should increase slowly as a function of the number of nodes (like O(log n) or O(log(log n)).Existing graph generation models do not exhibit these types of behavior, even at a qualitative level. We provide a new graph generator, based on a "forest fire" spreading process, that has a simple, intuitive justification, requires very few parameters (like the "flammability" of nodes), and produces graphs exhibiting the full range of properties observed both in prior work and in the present study. A majority of social network research deals with explicitly formed social networks. Although only rarely acknowledged for its existence, we believe that implicit social networks play a significant role in the overall dynamics of social networks. We propose a framework to evaluate the dynamics and characteristics of a set of explicit and associated implicit social networks. Specifically, we propose a social network matrix to measure the implicit relationships among the entities in various social networks. We also derive several indicators to characterize the dynamics in online social networks. We proceed by incorporating implicit social networks in a traditional network flow context to evaluate key network performance indicators such as the lowest communication cost, maximum information flow, and the budgetary constraints. We consider (online) implicit social networks.We propose a social network matrix to measure the implicit relationships among the entities in various social networks.We derive several indicators to characterize the dynamics in online social networks. Given a set of people and a set of events attended by them, we address the problem of measuring connectedness or tie strength between each pair of persons. The underlying assumption is that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms, which a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms. We then show that there is a range of tie-strength measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges of the social network (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. To settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order. This decision is best left to particular applications. We also classify existing tie-strength measures according to the axioms that they satisfy; and observe that none of the "self-referential" tie-strength measures satisfy the axioms. In our experiments, we demonstrate the efficacy of our approach; show the completeness and soundness of our axioms, and present Kendall Tau Rank Correlation between various tie-strength measures. Although users of online communication tools rarely categorize their contacts into groups such as "family", "co-workers", or "jogging buddies", they nonetheless implicitly cluster contacts, by virtue of their interactions with them, forming implicit groups. In this paper, we describe the implicit social graph which is formed by users' interactions with contacts and groups of contacts, and which is distinct from explicit social graphs in which users explicitly add other individuals as their "friends". We introduce an interaction-based metric for estimating a user's affinity to his contacts and groups. We then describe a novel friend suggestion algorithm that uses a user's implicit social graph to generate a friend group, given a small seed set of contacts which the user has already labeled as friends. We show experimental results that demonstrate the importance of both implicit group relationships and interaction-based affinity ranking in suggesting friends. Finally, we discuss two applications of the Friend Suggest algorithm that have been released as Gmail Labs features. This research draws on longitudinal network data from an online community to examine patterns of users' behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users' gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a “small world” characterized by the emergence, in its early stages, of a hub-dominated structure with heterogeneity in users' behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first-mover advantages underpinning users' ability to rise to system prominence, and uncover gender differences in users' gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems. © 2009 Wiley Periodicals, Inc.