@conference {
	title = {Bipartite Consensus for Global Trust in Social Network Services},
	author = {Gao, Peixin., Liu, Zhixin., Baras, John S.},
	year = {2015},
	month = {12/2015},
	abstract = {As social network services (SNS) are gaining popularity, establishing trust relationships among users in SNS is necessary for user interaction and decision making. However, the evaluation of global trust values (i.e. reputation) of users suffers from the curse of opinion divergence in the network. In this paper, we consider the scenario of opinion divergence within social networks, and propose a method to reach different global trust values between groups of users with controversial opinions in the network. We model the global trust opinion formation in discrete-time dynamics and introduce bipartite consensus as the approach to establish global trust in such circumstances. Such approach works upon the property of structural balance, and can be extended to more general situations where eventual positivity applies. Via our approach, non-trivial global trust can be reached within the network, which can guide users{\textquoteright} social behaviors, and support many SNS-based applications.},
	address = {San Diego, California},
	booktitle = {IEEE Global Communications Conference (Globecom 2015)}
}
@conference {
	title = {HyperCubeMap: Optimal Social Network Ad Allocation Using Hyperbolic Embedding },
	author = {Miao, Hui., Gao, Peixin., Hajiaghayi, MohammadTaghi., Baras, John S.},
	year = {2015},
	month = {08/2015},
	abstract = {Advertising activity on SNS has grown rapidly and is now a billion dollar business. In the SNS advertising model, the SNS serves as the advertising agent, and takes the advantage of network diffusion to attract advertisers and charges for the cascading impressions. The optimal ad allocation task is to choose the ad allocation plan that maximizes the revenue. Each user has different diffusion ability, limited daily impressions and the advertisers have various bidding prices and budget concerns. A feasible plan that obeys the constraints is difficult to find. The solution of this problem lies in the space of N |Ads|{\texttimes}|User| 0 , which makes direct optimization unattractive. In this paper, we study SNS advertising business models, formulate the SNS ad allocation problem and show their connections with hyperbolic embedding. We develop a new embedding algorithm HYPERCUBEMAP that allows for dimension reduction. Our proposed method reduces the dimensionality of the original problem significantly, runs two to four orders of magnitude faster, and reaches 95\% of the optimum},
	address = {Paris, France},
	booktitle = {The 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2015)},
	pages = {pp. 357-362}
}
@conference {
	title = {Semiring-Based Trust Evaluation for Information Fusion in Social Network Services},
	author = {Gao, Peixin., Baras, John S., Golbeck, Jennifer.},
	year = {2015},
	month = {07/2015},
	abstract = {As online social network services (SNS) are booming~and gaining tremendous popularity, there is a sharply increasing~amount of information exchange and interactions among SNS~users. Taking this advantage, users in SNS make decisions via~collecting and combining information from different sources~(i.e. other users). However, there exists a large variance of~trustworthiness among SNS populations, which is threatening the~quality of the information fusion process. In such circumstances,~trust relationships among users in SNS are very important in~decision making as well as for the success of many SNS-based applications, e.g. recommender systems and ad targeting. An~appropriate trust inference mechanism for trust evaluation is~necessary in extending the knowledge base of trust opinions and~tackle the issue of limited trust information due to link sparsity~in social networks. In this work, we model the trust relationship~among users in SNS as a 2-dimensional vector, and propose a semiring-based model for trust propagation and fusion as the~building block of our trust inference framework. Specifically, in~our approach, both trust and distrust (i.e., positive and negative~trust) are both considered, and opinion conflict resolution is supported by our framework of trust inference.},
	address = {Washington DC},
	booktitle = {International Conference on Information Fusion (FUSION 2015)},
	pages = {pp. 590-596}
}
@conference {
	title = {Trust-Aware Social Recommended System Design},
	author = {Gao, Peixin., Baras, John S., Golbeck, Jennifer.},
	year = {2015},
	month = {02/2015},
	address = {ESEOM Angers, Loire Valley, France},
	booktitle = {1st International Conference on Information Systems Security and Privacy},
	pages = {pp. 19-28}
}
