Bimodal invitation-navigation fair bets model for authority identification in a social network

Budalakoti, Suratna; Bekkerman, Ron

doi:10.1145/2187836.2187932

Cited by 16 publications

(10 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Budalakoti and Bekkerman [2012] studied the LinkedIn social network in order to build a ranked list of users based on estimated authority scores. To this end, the authors in [Budalakoti and Bekkerman 2012] constructed two directed graphs over the same set of users: (1) the invitation graph which is based on invitations to connect, and (2) the navigation graph which is based on users' browsing behaviour. The authority degree of users is then estimated by simultaneously using the authority scores of nodes in one graph to inform the other, and vice versa, in a mutually reinforcing fashion.…”

Section: Related Workmentioning

confidence: 99%

Identifying Authorities in Online Communities

Bouguessa

Romdhane

2015

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

Several approaches have been proposed for the problem of identifying authoritative actors in online communities. However, the majority of existing methods suffer from one or more of the following limitations: (1) there is a lack of an automatic mechanism to formally discriminate between authoritative and nonauthoritative users. In fact, a common approach to authoritative user identification is to provide a ranked list of users expecting authorities to come first. A major problem of such an approach is where to stop reading the ranked list of users? How many users should be chosen as authoritative? (2) supervised learning approaches for authoritative user identification suffer from their dependency on the training data. The problem here is that labeled samples are more difficult, expensive, and time consuming to obtain than unlabeled ones. (3) several approaches rely on some user parameters to estimate an authority score. Detection accuracy of authoritative users can be seriously affected if incorrect values are used. In this paper, we propose a parameterless mixture model-based approach which is capable of addressing the three aforementioned issues in a single framework. In our approach, we first represent each user with a feature vector composed of information related to its social behaviour and activity in an online community. Next, we propose a statistical framework, based on the multivariate beta mixtures, in order to model the estimated set of feature vectors. The probability density function is therefore estimated and the beta component that corresponds to the most authoritative users is identified. The suitability of the proposed approach is illustrated on real data extracted from Stack Exchange question-answering network and Twitter.

show abstract

Section: Related Workmentioning

confidence: 99%

Identifying Authorities in Online Communities

Bouguessa

Romdhane

2015

ACM Trans. Intell. Syst. Technol.

View full text Add to dashboard Cite

show abstract

“…Competing methods and notations: We compared our proposed methods against PageRank (henceforth PR), alpha-centrality (henceforth AC), and Fair-Bets model [7] (henceforth FB). The latter method, already mentioned in Section II, computes the score of any node i as r i = (1/out(i)) j∈B(i) r j .…”

Section: B Assessment Methodologymentioning

confidence: 99%

“…By contrast, lurking behaviors build on the amount of information a node receives; again, in Twitter terms, if user v follows user u, then v is benefiting from u's information (i.e., v is receiving u's tweets), whereby relationship is modeled as a link from u to v. Within this view, a key notion for modeling the mutual contribution from incoming and outgoing links appears to be the (weighted) in/out-degree ratio: in an influence-oriented graph, this would map to the follower-to-followee ratio of a user (so that, the higher this ratio, the higher the probability that the user is influential), whereas in a lurking-oriented graph, the strength of a user's lurking status would be proportional to her/his followee-to-follower ratio. It should be noted that the significance of leveraging the in/out-degree ratio for ranking purposes in SN was already identified in [7], although again to score the authority of nodes. Upon the in/out-degree ratio intuition, we now provide a basic definition of lurking which aims to lay out the essential hypotheses of a lurking status based solely on the topology information available in a SN.…”

Section: Topology-driven Lurkingmentioning

confidence: 98%

"Who's out there?"

Tagarelli

Interdonato

2013

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

View full text Add to dashboard Cite

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer-human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. Particularly, focusing on the network topology, we address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Using Twitter and FriendFeed as cases in point, our methods' performance was evaluated against data-driven rankings as well as existing centrality methods, including the classic PageRank and alphacentrality. Empirical evidence has shown the significance of our lurker ranking approach, which substantially differs from other methods in effectively identifying and ranking lurkers.

show abstract

“…"Visiting" measures how well engaged a user is (our assumption is that a frequent visitor is likely to interact with his/her social stream). "PageRank" (details in [4]) and "Connectedness" measure how a user connects with other users. Presumably, a highly respected and well connected user can attract others to interact with their update streams.…”

Section: Models and Featuresmentioning

confidence: 99%

Learning to rank social update streams

Liu

Bekkerman

Adler

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

As online social media further integrates deeper into our lives, we spend more time consuming social update streams that come from our online connections. Although social update streams provide a tremendous opportunity for us to access information on-the-fly, we often complain about its relevance. Some of us are flooded with a steady stream of information and simply cannot process it in full. Ranking the incoming content becomes the only solution for the overwhelmed users. For some others, in contrast, the incoming information stream is pretty weak, and they have to actively search for relevant information which is quite tedious. For these users, augmenting their incoming content flow with relevant information from outside their first-degree network would be a viable solution. In that case, the problem of relevance becomes even more prominent.In this paper, we start an open discussion on how to build effective systems for ranking social updates from a unique perspective of LinkedIn -the largest professional network in the world. More specifically, we address this problem as an intersection of learning to rank, collaborative filtering, and clickthrough modeling, while leveraging ideas from information retrieval and recommender systems. We propose a novel probabilistic latent factor model with regressions on explicit features and compare it with a number of non-trivial baselines. In addition to demonstrating superior performance of our model, we shed some light on the nature of social updates on LinkedIn and how users interact with them, which might be applicable to social update streams in general.

show abstract

Bimodal invitation-navigation fair bets model for authority identification in a social network

Cited by 16 publications

References 21 publications

Identifying Authorities in Online Communities

Identifying Authorities in Online Communities

"Who's out there?"

Learning to rank social update streams

Contact Info

Product

Resources

About