WSDM16: Temporal Formation and Evolution of Online Communities
•
2 likes•589 views
This document discusses research into modeling the temporal formation and evolution of online communities. It proposes: 1) Modeling each user's interests over time as a topic space, building a weighted graph between users based on topic similarity over time, and using graph clustering to identify communities of like-minded users. 2) Analyzing causality between community behaviors over time to better understand influence and predict future behaviors, noting challenges include establishing temporal precedence and improving predictions using causal information. The research aims to better understand how interests drive similar temporal behaviors between users to form online communities.
Recommended
Recommended
More Related Content
What's hot
What's hot (12)
Similar to WSDM16: Temporal Formation and Evolution of Online Communities
Similar to WSDM16: Temporal Formation and Evolution of Online Communities (20)
More from Hossein Fani
More from Hossein Fani (17)
Recently uploaded
Recently uploaded (20)
WSDM16: Temporal Formation and Evolution of Online Communities
- 1. Laboratory for systems, software and semantics (LS3) Temporal Formation And Evolution Of Online Communities @ H o s s e i n F a n i @ R y e r s o n U @ U N B 1
- 2. Topological Topical Topical + Topological Topical + Temporal 1) Community 2
- 4. Hypothesis Like-minded users exhibit similar temporal behavior towards similar topics due to interest priority. Steps 1. Model each user with a temporal multidimensional topic space 2. Build a weighted graph over users 3. Weigh the edges by pairwise 2-D cross correlation 4. Dographclusteringtofindsubgraphsaslike-mindedusercommunities 4
- 5. 5
- 6. 2) Causality Ci Cj Cause (G) Effect 6
- 8. Granger Axioms i) Temporal Precedence: cause Ci should precede their effects Cj in time ii) Information in cause Ci’s past should improve the prediction of the effect Cj, above and beyond the information contained in past of the effect Cj 8
- 9. 1) Community Evaluation Dataset Ground Truth Timestamp Prediction Scalable Overlapping Clustering 2) Causality Philosophical Issues Causality, Influence, Prediction Challenges 9
Editor's Notes
- Hello everyone, I am going to present my work, supposedly my thesis steps: …. My work is part of a project in the LS3 lab at Ryerson. My name is Hossein, PhD student at UNB and visiting student at Ryerson.
- As you know, Predicting users’ behaviour, interests, and influence are of interest within the realm of social networks due to the wide range of applications such as personalized recommendations and marketing campaigns. However, the proposed approaches are not always scalable to large number of users and huge amount of user-generated content. Community-level studies are introduced to help facilitate scalability, among others, highlighting the main properties of the network at higher collective macro level prespective So far different, various community detection method have been proposed in the literature Topology-based community detection methods focus on the explicit links, e.g. followership in Twitter, to detect like-minded users [1]. It follows the theory of homophily. the tendency of individuals to associate and bond with similar others. Since many of the explicit social connections are grounded in other factors, e.g. kinship, that do not necessarily point to inter-user interest similarity [2] and like-minded users are not necessarily explicitly connected to each other, content (topic)-based approaches are introduced [3, 4] . There are also hybrid approaches that incorporate both topology and content to identify a reliable account of like-minded communities In this work, we suggest, like-minded users not only share same interest, but also share similar temporal behaviour.
- If we have topics 1 to 50, detected by a topic detection method. We can simply show the contribution of users toward the detected topics within time by counting how many times the user mentions the significant words of that topic in each time steps. To make it more clear, consider two twitter users Antonio Fumero, and Edgar Barrera. As seen, both users are interested in topics …. However, with respect to the topic z12, they contribute differently. Antonio, the user on the left, starts his tweets about This topic from mid Nov to early dec. On the other hand, Edgar, the user on the right begins to post status about this topic with a time shift, from early ,,, This may happen due to their interest priority, or type of interest. Contempranouse (instantaneously) Vs. Analytical With the non-temporal approaches, these two users will finally be members of a same community. the approaches in [3, 4, 5] do not take into account the fact that like-minded users need to exhibit similar temporal behaviour towards similar topics as well. This is crucial in applications such as news recommendations. it would be unreasonable to recommend a news article on topic A to users who discussed topic A one week ago but would make total sense to recommend the same article to users who are currently actively pursuing topic A on Twitter at the current point in time
- In order to address this type of community detection, we First model each user within time domain and topic space. So, we have a multidimentinal signal or multivariate time series. Then we calculate the pairwise signal similarity by 2D-X-Cor. Intuitively, the 2D cross-correlation measure slides one matrix over the other and sums up the multiplications of the overlapping elements Next, we create a weighted graph whose nodes are the users, and the weight over the edges are the respective x-cor score. Finally we apply graph clustering methods, Louvain or VOS, to find the cohesive subgraphs as communities of like-minded users
- We submitted this step to this conference. The reviewers were interested in the idea however the evaluation part was not satisfactory. We have been working on creating a new evalution methodology, news recommendation and it is now under review in ICWSM As you see, the remporal way of community detection with a particular topic detection method imporoves the metrics in recommendations. CD: topical community detection We may discuss about this evaluation setup later.
- The 2nd step in my work is investigating inter-community relations, particularly causation (causality) Motivation Causation provides systematic explanation as to why communities are formed and helps to predict future communities’ trend or detrends. To the best of our knowledge, no such study has been done. i.e., sudy the causal relationships among the like-minded communities in Twitter.
- As you may know, granger concept of causality is a seminal work in casual study over time series, esp in economics Granger causality has two axioms: temporal precedence. The cause should always precede the effect in time Secondly, the past history of the cause should help to predict the future of the effect. If we predict the future of the effect only on its own past, it is called auto regression. In another word, granger says the cause should result in a better prediction than auto regression. When I applied granger to our detected communiyies in the previous step, there are a lot of seemingly nonsence (spurious, fake) causality amongst the communities. Granger Causality is not meant to be equivalent to true causality (what is true causality), but is merely intended to provide useful information regarding causation. multivariate extension, referred to as conditional G-causality
- Timestamp prediction, How?