Results 1 - 10
of
13
Assessing and Ranking Structural Correlations in Graphs
"... Real-life graphs not only have nodes and edges, but also have events taking place, e.g., product sales in social networks and virus infection in communication networks. Among different events, some exhibit strong correlation with the network structure, while others do not. Such structural correlatio ..."
Abstract
-
Cited by 10 (2 self)
- Add to MetaCart
(Show Context)
Real-life graphs not only have nodes and edges, but also have events taking place, e.g., product sales in social networks and virus infection in communication networks. Among different events, some exhibit strong correlation with the network structure, while others do not. Such structural correlation will shed light on viral influence existing in the corresponding network. Unfortunately, the traditional association mining concept is not applicable in graphs since it only works on homogeneous datasets like transactions and baskets. We propose a novel measure for assessing such structural correlations in heterogeneous graph datasets with events. The measure applies hitting time to aggregate the proximity among nodes that have the same event. In order to calculate the correlation scores for many events in a large network, we develop a scalable framework, called gScore, using sampling and approximation. By comparing to the situation where events are randomly distributed in the same network, our method is able to discover events that are highly correlated with the graph structure. gScore is scalable and was successfully applied to the co-author DBLP network and social networks extracted from TaoBao.com, the largest online shopping network in China, with many interesting discoveries.
Wisdom of the Better Few: Cold Start Recommendation via Representative based Rating Elicitation
"... Recommender systems have to deal with the cold start problem as new users and/or items are always present. Rating elicitation is a common approach for handling cold start. However, there still lacks a principled model for guiding how to select the most useful ratings. In this paper, we propose a pri ..."
Abstract
-
Cited by 8 (0 self)
- Add to MetaCart
(Show Context)
Recommender systems have to deal with the cold start problem as new users and/or items are always present. Rating elicitation is a common approach for handling cold start. However, there still lacks a principled model for guiding how to select the most useful ratings. In this paper, we propose a principled approach to identify representative users and items using representative-based matrix factorization. Not only do we show that the selected representatives are superior to other competing methods in terms of achieving good balance between coverage and diversity, but we also demonstrate that ratings on the selected representatives are much more useful for making recommendations (about 10 % better than competing methods). In addition to illustrating how representatives help solve the cold start problem, we also argue that the problem of finding representatives itself is an important problem that would deserve further investigations, for both its practical values and technical challenges.
CASINO: Towards Conformity-aware Social Influence Analysis in Online Social Networks
"... Social influence analysis in online social networks is the study of people’s influence by analyzing the social interactions between individuals. In recent years, there have been increasing research efforts to understand the influence propagation phenomenon due to its importance to viral marketing an ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
(Show Context)
Social influence analysis in online social networks is the study of people’s influence by analyzing the social interactions between individuals. In recent years, there have been increasing research efforts to understand the influence propagation phenomenon due to its importance to viral marketing and information dissemination among others. Despite the progress achieved by state-of-the-art social influence analysis techniques, a key limitation of these techniques is that they only utilize positive interactions (e.g., agreement, trust) between individuals, ignoring two equally important factors, namely, negative relationships (e.g., distrust, disagreement) between individuals and conformity of people, which refers to a person’s inclination to be influenced. In this paper, we propose a novel algorithm for social influence analysis called casino (Conformity-Aware Social INfluence cOmputation), which quantitatively studies the interplay between influence and conformity of each individual by exploiting the positive and negative relationships between individuals. Given a social network, casino first extracts a set of topic-based subgraphs where each subgraph depicts the social interactions between individuals associated with a specific topic. Then it optionally labels the edges (relationships) between individuals with positive or negative signs. Finally, it iteratively computes the influence and conformity indices of each individual in each signed topic-based subgraph. Our exhaustive empirical study with several real-world social networks demonstrates superior effectiveness and accuracy of casino for social influence analysis compared to state-of-the-art methods. Furthermore, our investigation revealed several interesting characteristics of “influentials ” and “conformers ” in these social networks. 2 1
Learning relevance from heterogeneous social network and its application in online targeting
- In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval (SIGIR’11
, 2011
"... The rise of social networking services in recent years presents new research challenges for matching users with interesting content. While the content-rich nature of these social networks offers many cues on “interests ” of a user such as text in user-generated content, the links in the network, and ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
(Show Context)
The rise of social networking services in recent years presents new research challenges for matching users with interesting content. While the content-rich nature of these social networks offers many cues on “interests ” of a user such as text in user-generated content, the links in the network, and user demographic information, there is a lack of successful methods for combining such heterogeneous data to model interest and relevance. This paper proposes a new method for modeling user interest from heterogeneous data sources with distinct but unknown importance. The model leverages links in the social graph by integrating the conceptual representation of a user’s linked objects. The proposed method seeks a scalable relevance model of user interest, that can be discriminatively optimized for various relevance-centric problems, such as Internet advertisement selection, recommendation, and web search personalization. We apply our algorithm to the task of selecting relevant ads for users on Facebook’s social network. We demonstrate that our algorithm can be scaled to work with historical data for all users, and learns interesting associations between concept classes automatically. We also show that using the learnt user model to predict the relevance of an ad is the single most important signal in our ranking system for new ads (with no historical clickthrough data), and overall leads to an improvement in the accuracy of the clickthrough rate prediction, a key problem in online advertising.
Microscopic Social Influence
"... Social influences, the phenomena that one individual’s actions can induce similar behaviors among his/her friends via their social ties, have been observed prevail-ingly in socially networked systems. While most exist-ing work focuses on studying general, macro-level influ-ence (e.g., diffusion); eq ..."
Abstract
-
Cited by 2 (0 self)
- Add to MetaCart
(Show Context)
Social influences, the phenomena that one individual’s actions can induce similar behaviors among his/her friends via their social ties, have been observed prevail-ingly in socially networked systems. While most exist-ing work focuses on studying general, macro-level influ-ence (e.g., diffusion); equally important is to understand social influence at microscopic scales (i.e., at the gran-ularity of single individuals, actions, and time-stamps), which may benefit a range of applications. We propose µSI, a microscopic social-influence model wherein: indi-viduals ’ actions are modeled as temporary interactions between social network (formed by individuals) and ob-ject network (formed by targets of actions); one indi-vidual’s actions influence his/her friends in a dynamic, network-wise manner (i.e., dependent on both social and object networks). We develop for µSI a suite of novel inference tools that enable to answer questions of the form: How may an occurred interaction trigger another? More impor-tantly, when and where may a new interaction be ob-served? We carefully address the computational chal-lenges for inferencing over such semantically rich mod-els by dynamically identifying sub-domains of interest and varying the precision of solutions over different sub-domains. We demonstrate the breadth and generality of µSI using two seemingly disparate applications. In the context of social tagging service, we show how it can help improve the accuracy and freshness of resource recommendation; in the context of mobile phone call service, we show how it can help improve the efficiency of paging operation. 1
Speeding up Large-Scale Learning with a Social Prior
"... Amazon Inc.(Work done while at Facebook Inc.) ..."
(Show Context)
Measuring Two-Event Structural Correlations on Graphs
"... Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlatio ..."
Abstract
- Add to MetaCart
(Show Context)
Real-life graphs usually have various kinds of events happening on them, e.g., product purchases in online social networks and intrusion alerts in computer networks. The occurrences of events on the same graph could be correlated, exhibiting either attraction or repulsion. Such structural correlations can reveal important relationships between different events. Unfortunately, correlation relationships on graph structures are not well studied and cannot be captured by traditional measures. In this work, we design a novel measure for assessing twoevent structural correlations on graphs. Given the occurrences of two events, we choose uniformly a sample of “reference nodes ” from the vicinity of all event nodes and employ the Kendall’s τ rank correlation measure to compute the average concordance of event density changes. Significance can be efficiently assessed by τ’s nice property of being asymptotically normal under the null hypothesis. In order to compute the measure in large scale networks, we develop a scalable framework using different sampling strategies. The complexity of these strategies is analyzed. Experiments on real graph datasets with both synthetic and real events demonstrate that the proposed framework is not only efficacious, but also efficient and scalable. 1.
Optimizing Display Advertising in Online Social Networks
"... Advertising is a significant source of revenue for most online social networks. Conventional online advertising methods need to be cus-tomized for online social networks in order to address their distinct characteristics. Recent experimental studies have shown that pro-viding social cues along with ..."
Abstract
- Add to MetaCart
(Show Context)
Advertising is a significant source of revenue for most online social networks. Conventional online advertising methods need to be cus-tomized for online social networks in order to address their distinct characteristics. Recent experimental studies have shown that pro-viding social cues along with ads, e.g. information about friends liking the ad or clicking on an ad, leads to higher click rates. In other words, the probability of a user clicking an ad is a function of the set of friends that have clicked the ad. In this work, we propose formal probabilistic models to capture this phenomenon, and study the algorithmic problem that then arises. Our work is in the context of display advertising where a contract is signed to show an ad to a pre-determined number of users. The problem we study is the fol-lowing: given a certain number of impressions, what is the optimal display strategy, i.e. the optimal order and the subset of users to show the ad to, so as to maximize the expected number of clicks? Unlike previous models of influence maximization, we show that this optimization problem is hard to approximate in general, and that it is related to finding dense subgraphs of a given size. In light of the hardness result, we propose several heuristic algorithms including a two-stage algorithm inspired by influence-and-exploit strategies in viral marketing. We evaluate the performance of these heuristics on real data sets, and observe that our two-stage heuristic significantly outperforms the natural baselines. 1.
Extracting Top-k Most Influential Nodes by Activity Analysis
"... Can we statistically compute social influence and under-stand quantitatively to what extent people are likely to be influenced by the opinion or the decision of their friends, friends of friends, or acquaintances? An in-depth under-standing of such social influence and the diffusion process of such ..."
Abstract
- Add to MetaCart
(Show Context)
Can we statistically compute social influence and under-stand quantitatively to what extent people are likely to be influenced by the opinion or the decision of their friends, friends of friends, or acquaintances? An in-depth under-standing of such social influence and the diffusion process of such social influence will help us better address the ques-tion of to what extent the ’word of mouth ’ effects will take hold on social networks. Most of the existing social in-fluence models to define the influence diffusion are solely based on topological connectivity of social network nodes. In this paper, we presented an activity-base social influence model. Our experimental results show that activity-based social influence is more effective in understanding the viral marketing effects on social networks. 1
JOURNAL OF TRANSACTIONS ON SERVICES COMPUTING: SPECIAL ISSUE, TSC MANUSCRIPT UNDER CONSIDERATION 1 Probabilistic Diffusion of Social Influence with Incentives
"... Abstract—With explosive growth of social media, social computing becomes a new IT feature. A core functionality of social computing is social network analysis, which studies dynamics of social connectivity among people, including how people influence one another and how fast information diffuses in ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—With explosive growth of social media, social computing becomes a new IT feature. A core functionality of social computing is social network analysis, which studies dynamics of social connectivity among people, including how people influence one another and how fast information diffuses in a social network and what factors stimulate influence diffusion. One of the models for information diffusion is the heat diffusion model. Although it is simple in capturing the basic principle of social influence, there are several limitations. First, the uniform heat diffusion is no longer hold in social networks. Second, high degree nodes are most influential in all contexts is not realistic. In this paper we propose a probabilistic approach of social influence diffusion model with incentives. Our approach has three features. First we define an influence diffusion probability for each node instead of uniform probability. Second, we categorize nodes into two classes: active and inactive. Active nodes have chances to influence inactive nodes but not vice versa. Third, we utilize a system defined diffusion threshold to control how influence is propagated. We study how incentives can be utilized to boost the influence diffusion. Our experiments show the reward-powered model is more effective in influence diffusion.
