Evolution of an online social aggregation network

Garg, Sanchit; Gupta, Trinabh; Carlsson, Niklas; Mahanti, Anirban

doi:10.1145/1644893.1644931

Cited by 41 publications

(40 citation statements)

References 21 publications

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“…In this case, more nodes are in the result graphs, and updated edges are less likely to affect the match results. The results also demonstrate the potential benefits of minDelta in real-life applications where insertions are much more common (e.g., [12]). …”

Section: Exp-3: Optimization Techniquesmentioning

confidence: 59%

“…We utilize the k-betweeness centrality metric of [31] for landmark selections in our algorithms, and develop incremental maintenance algorithms for weighted landmarks. In our experimental study we take into account the densification law [17] and relation generation models [12], which simulate the evolution of real-life networks.…”

Section: Examplementioning

confidence: 99%

“…Graphs are controlled by three parameters: the number of nodes |V |, the number of edges |E| and the average number |att| of attributes of a node. We produced sequences of data graphs following the densification law [17] and linkage generation models [12]. We used two parameters to control updates: (a) update type (edge insertion or deletion), and (b) the size of updates |ΔG|.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In practice, a data graph G is typically large, and moreover, is frequently updated. This is particularly evident in, e.g., social networks [12], Web graphs [18] and traffic networks [4]. It is often prohibitively expensive to recompute the matches starting from scratch when G is updated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Incremental graph pattern matching

Fan

Luo

et al. 2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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Graph pattern matching has become a routine process in emerging applications such as social networks. In practice a data graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches from scratch via batch algorithms when the graph is updated. With this comes the need for incremental algorithms that compute changes to the matches in response to updates, to minimize unnecessary recomputation. This paper investigates incremental algorithms for graph pattern matching defined in terms of graph simulation, bounded simulation and subgraph isomorphism. (1) For simulation, we provide incremental algorithms for unit updates and certain graph patterns. These algorithms are optimal: in linear time in the size of the changes in the input and output, which characterizes the cost that is inherent to the problem itself. For general patterns we show that the incremental matching problem is unbounded, i.e., its cost is not determined by the size of the changes alone. (2) For bounded simulation, we show that the problem is unbounded even for unit updates and path patterns. (3) For subgraph isomorphism, we show that the problem is intractable and unbounded for unit updates and path patterns. (4) For multiple updates, we develop an incremental algorithm for each of simulation, bounded simulation and subgraph isomorphism. We experimentally verify that these incremental algorithms significantly outperform their batch counterparts in response to small changes, using real-life data and synthetic data.

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Section: Exp-3: Optimization Techniquesmentioning

confidence: 59%

Section: Examplementioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental graph pattern matching

Fan

Luo

et al. 2011

Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data

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“…They use both the API and website crawling to collect this data. Garg et al [13] examine the evolution of the OSN FriendFeed by collecting data on more than 200,000 users with the FriendFeed API, along with close to four million directed edges among them. Rejaie et al [36] estimate the size of active users on Twitter and MySpace by collecting data on a random sample of users through the API.…”

Section: Osn Characteristicsmentioning

confidence: 99%

Reliable Online Social Network Data Collection

Abdesslem

Parris

Henderson

2012

Computational Social Networks

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Large quantities of information are shared through online social networks, making them attractive sources of data for social network research. When studying the usage of online social networks, these data may not describe properly users' behaviours. For instance, the data collected often include content shared by the users only, or content accessible to the researchers, hence obfuscating a large amount of data that would help understanding users' behaviours and privacy concerns. Moreover, the data collection methods employed in experiments may also have an effect on data reliability when participants self-report inacurrate information or are observed while using a simulated application. Understanding the effects of these collection methods on data reliability is paramount for the study of social networks; for understanding user behaviour; for designing socially-aware applications and services; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies, and introduce our own methodology and user study based on the Experience Sampling Method; we claim our methodology leads to the collection of more reliable data by capturing both those data which are shared and not shared. We conclude with suggestions for collecting and mining data from online social networks.

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Measurement Methods of User Behavior in Online Social Networks

Gyarmati

Trinh

2012

Computational Social Networks

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Evolution of an online social aggregation network

Cited by 41 publications

References 21 publications

Incremental graph pattern matching

Incremental graph pattern matching

Reliable Online Social Network Data Collection

Measurement Methods of User Behavior in Online Social Networks

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