Spam is a serious problem plaguing web-scale digital platforms which facilitate user content creation and distribution. It compromises platform's integrity, performance of services like recommendation and search, and overall business. Spammers engage in a variety of abusive and evasive behavior which are distinct from non-spammers. Users' complex behavior can be well represented by a heterogeneous graph rich with node and edge attributes. Learning to identify spammers in such a graph for a web-scale platform is challenging because of its structural complexity and size. In this paper, we propose SEINE (Spam DEtection using Interaction NEtworks), a spam detection model over a novel graph framework. Our graph simultaneously captures rich users' details and behavior and enables learning on a billion-scale graph. Our model considers neighborhood along with edge types and attributes, allowing it to capture a wide range of spammers. SEINE, trained on a real dataset of tens of millions of nodes and billions of edges, achieves a high performance of 80% recall with 1% false positive rate. SEINE achieves comparable performance to the state-of-the-art techniques on a public dataset while being pragmatic to be used in a large-scale production system.

Keyword-based search in text-rich multi-dimensional datasets facilitates many novel applications and tools. In this paper, we consider objects that are tagged with keywords and are embedded in a vector space. For these datasets, we study queries that ask for the tightest groups of points satisfying a given set of keywords. We propose a novel method called ProMiSH (Projection and Multi Scale Hashing) that uses random projection and hash-based index structures, and achieves high scalability and speedup. We present an exact and an approximate version of the algorithm. Our experimental results on real and synthetic datasets show that ProMiSH has up to 60 times of speedup over state-of-the-art tree-based techniques.

Given a collection of binary constraints that categorize whether a data object is relevant or not, we consider the problem of online retrieval of the top-k objects that best represent all other relevant objects in the underlying dataset. Such top-k representative queries naturally arise in a wide range of complex data analytic applications including advertisement, search, and recommendation. In this paper, we aim at identifying the top-k representative objects that are high-scoring, satisfy diverse subsets of given binary constraints, as well as representative of various other relevant objects in the dataset. We formulate our problem with the well-established notion of the top-k representative skylines, and we show that the problem is NP-hard. Hence, we design efficient techniques to solve our problem with theoretical performance guarantees. As a side-product of our algorithm, we also improve the asymptotic time-complexity of skyline computation to log-linear time in the number of data points when all dimensions except one are binary in nature. Our empirical results attest that the proposed method efficiently finds high-quality top-k representative objects, while our technique is one order of magnitude faster than state-of-the-art methods for finding the top-k skylines with binary constraints.

Near neighbor search in high dimensional spaces is useful in many applications. Existing techniques solve this problem efficiently only for the approximate cases. These solutions are designed to solve r-near neighbor queries for a fixed query range or for a set of query ranges with probabilistic guarantees, and then extended for nearest neighbor queries. Solutions supporting a set of query ranges suffer from prohibitive space cost. There are many applications which are quality sensitive and need to efficiently and accurately support near neighbor queries for all query ranges. In this paper, we propose a novel indexing and querying scheme called Spatial Intersection and Metric Pruning (SIMP). It efficiently supports r-near neighbor queries in very high dimensional spaces for all query ranges with 100% quality guarantee and with practical storage costs. Our empirical studies on three real datasets having dimensions between 32 and 256 and sizes up to 10 million show a superior performance of SIMP over LSH, Multi-Probe LSH, LSB tree, and iDistance. Our scalability tests on real datasets having as many as 100 million points of dimensions up to 256 establish that SIMP scales linearly with query range, dataset dimension, and dataset size.

Image ranking has long been studied, yet it remains a very challenging problem. Increasingly, online images come with additional metadata such as user annotations and geographic coordinates. They provide rich complementary information. We propose to combine such multimodal information through a unified hypergraph to improve image retrieval performance. Hypergraphs allow for the simultaneously capture of higher order relationships among images using different modalities, e.g. visual content, user tags, and geolocations. Each image is represented as a vertex in the hypergraph. Each hyperedge is formed by a vertex and it's k-nearest neighbors. Three types of hyperedges exist in our unified hypergraph, which are in correspondence to the three different modalities. Image ranking is then formulated as a ranking problem on a unified hypergraph. The proposed method can easily be extended to incorporate additional modalities as long as a similarity function exists to compare the features. Experimental results on large datasets are promising.

Spatial data are common in many scientific and commercial domains such as geographical information systems and gene/protein expression profiles. Querying for distribution patterns on such data can discover underlying spatial relationships and suggest avenues for further scientific exploration. Supporting such pattern retrieval requires not only the formulation of an appropriate scoring function for defining relevant connected subregions, but also the design of new access methods that can scale to large databases. In this paper, we propose a solution to this problem of querying significant sub-regions on spatial data provided as raster images. We design a scoring scheme to measure the similarity of subregions. All the raster images are tiled and each alignment of the query and a database image produces a tile score matrix. We show that the problem of finding the best connected subregion from this matrix is NP-hard and develop a dynamic programming heuristic. With this heuristic, we develop two index-based scalable search strategies, TARS and SPARS, to query patterns in large data repositories. Experimental results on real image datasets show that TARS offers an 87% improvement for small queries, and SPARS a 52% improvement in runtime for large queries, as compared to linear search. Qualitative tests on real datasets achieve precision of more than 80%.

We present an efficient and accurate method for duplicate video detection in a large database using video fingerprints. We have empirically chosen the color layout descriptor, a compact and robust frame-based descriptor, to create fingerprints which are further encoded by vector quantization (VQ). We propose a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and experimentally show its superior performance over other distance measures for accurate duplicate detection. Efficient search cannot be performed for high-dimensional data using a nonmetric distance measure with existing indexing techniques. Therefore, we develop novel search algorithms based on precomputed distances and new dataset pruning techniques yielding practical retrieval times. We perform experiments with a database of 38 000 videos, worth 1600 h of content. For individual queries with an average duration of 60 s (about 50% of the average database video length), the duplicate video is retrieved in 0.032 s, on Intel Xeon with CPU 2.33 GHz, with a very high accuracy of 97.5%.