text: We present in this paper a novel approach for as-you-type top- @math keyword search over social media. We adopt a natural "network-aware" interpretation for information relevance, by which information produced by users who are closer to the seeker is considered more relevant. In practice, this query model poses new challenges for effectiveness and efficiency in online search, even when a complete query is given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and classic IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate it through extensive experiments for several applications and search scenarios, including searching for posts in micro-blogging (Twitter and Tumblr), as well as searching for businesses based on reviews in Yelp. They show that our solution is effective in answering real-time as-you-type searches over social media.
  @cite_27: In this paper, we study a new problem of instant social graph search, which aims to find a sub graph that closely connects two and more persons in a social network. This is a natural requirement in our real daily life, such as "Who can be my referrals for applying for a job position?". In this paper, we formally define the problem and present a series of approximate algorithms to solve this problem: Path, Influence, and Diversity. To evaluate the social graph search results, we have developed two prototype systems, which are online available and have attracted thousands of users. In terms of both user's viewing time and the number of user clicks, we demonstrate that the three algorithms can significantly outperform (+34.56 -+131.37 ) the baseline algorithm.
  @cite_1: 
  @cite_17: Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of commodity servers. Unicorn is based on standard concepts in information retrieval, but it includes features to promote results with good social proximity. It also supports queries that require multiple round-trips to leaves in order to retrieve objects that are more than one edge away from source nodes. Unicorn is designed to answer billions of queries per day at latencies in the hundreds of milliseconds, and it serves as an infrastructural building block for Facebook's Graph Search product. In this paper, we describe the data model and query language supported by Unicorn. We also describe its evolution as it became the primary backend for Facebook's search offerings.
  @cite_8: In this paper we study approximate landmark-based methods for point-to-point distance estimation in very large networks. These methods involve selecting a subset of nodes as landmarks and computing offline the distances from each node in the graph to those landmarks. At runtime, when the distance between a pair of nodes is needed, it can be estimated quickly by combining the precomputed distances. We prove that selecting the optimal set of landmarks is an NP-hard problem, and thus heuristic solutions need to be employed. We therefore explore theoretical insights to devise a variety of simple methods that scale well in very large networks. The efficiency of the suggested techniques is tested experimentally using five real-world graphs having millions of edges. While theoretical bounds support the claim that random landmarks work well in practice, our extensive experimentation shows that smart landmark selection can yield dramatically more accurate results: for a given target accuracy, our methods require as much as 250 times less space than selecting landmarks at random. In addition, we demonstrate that at a very small accuracy loss our techniques are several orders of magnitude faster than the state-of-the-art exact methods. Finally, we study an application of our methods to the task of social search in large graphs.
summary: Person (or people) search represents another facet of social search'', related to this paper, as the task of finding highly relevant persons for a given seeker and keywords. Usually, the approach used in this type of application is to identify the most relevant users, and then to filter them by the query keywords @cite_8 @cite_1 . In this area, @cite_17 describes the main aspects of the Unicorn system for search over the Facebook graph, including a typeahead feature for user search. A similar search problem, finding a sub-graph of the social network that connects two or more persons, is considered under the instant search paradigm in @cite_27 .