Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

Method for sharing relationship information stored in a social network database with third party databases

Abstract: Social network information maintained in a first database is shared with a second database. The operators of the second database use the social network information to better manage services provided to their customers and target particular information to their customers. The process begins with a request made to an application server of the first database by an application server of the second database, for social network information relevant to a set of individuals. The request includes identifying information of each individual in the set. The first database is then searched for matches with the identifying information in the request. If matches are found, the social network information relevant to those individuals for whom matches are found is shared with the second database.

Time Constrained Influence Maximization in Social Networks

Abstract: Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing """"Word-of-Mouth"""" effect in social networks. Influence maximization problem has been extensively studied recently. However, none of the previous work considers the time constraint in the influence maximization problem. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm with performance guarantees. To improve the algorithm scalability, we propose two Influence Spreading Path based methods. Extensive experiments conducted over four public available datasets demonstrate the efficiency and effectiveness of the Influence Spreading Path based methods.

Cross-X Learning for Fine-Grained Visual Categorization

Abstract: Recognizing objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation. Recent work tackles this problem in a weakly-supervised manner: object parts are first detected and the corresponding part-specific features are extracted for fine-grained classification. However, these methods typically treat the part-specific features of each image in isolation while neglecting their relationships between different images. In this paper, we propose Cross-X learning, a simple yet effective approach that exploits the relationships between different images and between different network layers for robust multi-scale feature learning. Our approach involves two novel components: (i) a cross-category cross-semantic regularizer that guides the extracted features to represent semantic parts and, (ii) a cross-layer regularizer that improves the robustness of multi-scale features by matching the prediction distribution across multiple layers. Our approach can be easily trained end-to-end and is scalable to large datasets like NABirds. We empirically analyze the contributions of different components of our approach and demonstrate its robustness, effectiveness and state-of-the-art performance on five benchmark datasets. Code is available at \url{https://github.com/cswluo/CrossX}.

Systems and methods for automatically locating web-based social network members

Abstract: Systems and methods for automatically locating web-based social network members are provided. According to one embodiment, contact content including an associated GPS identifier and status for web-based social network members located at or near the same location automatically appears on a GPS-enabled device. A further exemplary system includes a GPS-enabled device configured to receive a GPS identifier and a status representing a location and a current state for a web-based social network member, a processing module that associates the received GPS-identifier and the received status, and a communications module that sends the associated GPS-identifier and status to a server comprising a web-based social network database. Contact content in a web-based social network database record in the web-based social network database is updated to include the associated GPS identifier and status for the web-based social network member.

Origins of coevolution between residues distant in protein 3D structures.

Abstract: Residue pairs that directly coevolve in protein families are generally close in protein 3D structures. Here we study the exceptions to this general trend-directly coevolving residue pairs that are distant in protein structures-to determine the origins of evolutionary pressure on spatially distant residues and to understand the sources of error in contact-based structure prediction. Over a set of 4,000 protein families, we find that 25% of directly coevolving residue pairs are separated by more than 5 A in protein structures and 3% by more than 15 A. The majority (91%) of directly coevolving residue pairs in the 5-15 A range are found to be in contact in at least one homologous structure-these exceptions arise from structural variation in the family in the region containing the residues. Thirty-five percent of the exceptions greater than 15 A are at homo-oligomeric interfaces, 19% arise from family structural variation, and 27% are in repeat proteins likely reflecting alignment errors. Of the remaining long-range exceptions (<1% of the total number of coupled pairs), many can be attributed to close interactions in an oligomeric state. Overall, the results suggest that directly coevolving residue pairs not in repeat proteins are spatially proximal in at least one biologically relevant protein conformation within the family; we find little evidence for direct coupling between residues at spatially separated allosteric and functional sites or for increased direct coupling between residue pairs on putative allosteric pathways connecting them.