@conference {
	title = {STAR : Semiring Trust Inference for Trust - Aware Social recommenders},
	author = {Gao, Peixin., Miao, Hui., Baras, John S., Golbeck, Jennifer.},
	year = {2016},
	month = {09/2016},
	abstract = {Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationships are represented by 2-D vectors. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.},
	address = {Boston, Massachusetts},
	booktitle = {10th ACM Conference on Recommender Systems},
	pages = {pp. 301-308}
}
@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}
}
@conference {
	title = {Social Network Ad Allocation via Hyperbolic Embedding},
	author = {Gao, Peixin., Miao, Hui., Baras, John S.},
	year = {2014},
	month = {12/2014},
	abstract = {With the increasing popularity and ubiquity of online social networks (SNS), many advertisers choose to post their advertisements (Ads) within SNS. As a central problem for Ad platforms, Ad allocation is to maximize its revenue without overcharging advertisers, and it has received increasing attention from both industry and academia. The offline approach is a high dimensional integer programming problem with constraints incorporating potential allocation requirements from advertisers. In this paper we investigate the SNS Ad allocation problem in a single target group setting, study the connection of SNS advertising and hyperbolic geometry, and propose an approximation using hyperbolic embedding, which not only reduces the dimensionality of SNS Ad allocation problem significantly, but also provides a general framework for designing allocation strategies incorporating business rules. We evaluate the optimality and efficiency of our approach.},
	booktitle = {53rd IEEE Conference on Decision and Control},
	pages = {pp. 4875-4880},
	organization = {Los Angeles, California, USA.}
}
@conference {
	title = {Distributed Consensus with Byzantine Adversaries},
	author = {Liu, Xiangyang., Gao, Peixin., Baras, John S.},
	year = {2013},
	month = {10/2013},
	abstract = {The problem of distributed consensus algorithms in the presence of Byzantine adversaries has been increasingly important in both distributed computing scenaria and cyber-physical systems. Yet a satisfactory solution to the problem is still being sought due to the fact that Byzantine adversaries can output arbitrary wrong states and even collude to cause worse damages. We aim to solve the problem via detecting adversaries. Related works in multi-party computation in computer science are addressing this type of problem relying on the assumption that adversaries cannot lie about their input values. Works in the control theoretic literature rely on strict and hardly verifiable network robustness conditions. Both lines of research just consider guaranteeing the consensus in the face of adversaries yet neither of them considers detecting adversaries. In this paper a method is proposed to investigate this problem based on the detection of adversaries in the execution of the consensus algorithm. We integrate a trust evaluation mechanism into the consensus algorithm, where trust can be established with the help of a special subset of nodes, namely headers. We propose a two-layer hierarchical framework, where the top layer is a super-step dealing with a vectorized consensus algorithm, and the bottom layer is a sub-step, executing our proposed parallel vectorized voting scheme. The parallel vectorized voting scheme uses the information received by the top layer as an initial trust opinion and then evolves to equilibrium trust opinions. The vectorized consensus algorithm in the top layer utilizes the trust evaluation results provided by the bottom layer to update confidence levels and states. We also derive an upper bound of the number of adversaries that our algorithm can resist in each super-step.},
	address = {Montecello, Illinois},
	booktitle = {Proceedings of 51st Annual Allerton Conference on Communication, Control and Computing}
}
@article {
	title = {Social Network Ad Allocation and Optimization: A Hyperbolic Embedding-Based Approach},
	author = {Gao, Peixin., Miao, Hui., Baras, John S., Hajiaghayi, MohammadTaghi.},
	year = {2015},
	month = {12/2015},
	abstract = {With the increasing popularity of online social networks (SNS), many advertisers choose to post their advertisements (ads) within SNS. Advertising activity on social network sites (SNS) has grown rapidly and is now a billion-dollar business. For example, Facebook has reached more than 1 million active advertisers who contribute 90\% of its revenue. In the SNS advertising model, advertisers participating in a SNS ad campaign benefit from the effects of viral marketing and network diffusion. Modern SNS serve as advertising agents, and take advantage of the network diffusion to attract advertisers and charge for the cascading impressions. The optimal ad allocation task is the problem of choosing the ad allocation plan that maximizes revenue for the SNS. Considering that users have diffusion abilities and limited daily impressions, and advertisers have various bidding prices and budget concerns, a feasible plan that obeys the constraints is difficult to find. The solution to this problem lies in the space of N 0 |Ads|{\texttimes}|User|, which makes direct optimization unattractive. In this work, we study SNS advertising business models and formulate the SNS ad allocation problem. We show its connection with hyperbolic embedding and how the SNS ad allocation problem can be formulated as a linear program based on the hyperbolic embedding of a complex network. Accordingly, we develop a new embedding algorithm HyperCubeMap and an~optimization framework that allow for dimension reduction in the optimization process. Our proposed method is able to reduce the dimensionality of the original problem significantly, run two to four orders of magnitude faster, and reach 95\% of the optimal solution. We also discuss the possible extensions based on our current optimization framework, including shape design in allocation strategies for domain constraints like fairness and more comprehensive social influence models, in order to incorporate more real-world requirements.},
	journal = {Social Network Analysis and Mining}
}
