259 research outputs found
Exact Single-Source SimRank Computation on Large Graphs
SimRank is a popular measurement for evaluating the node-to-node similarities
based on the graph topology. In recent years, single-source and top- SimRank
queries have received increasing attention due to their applications in web
mining, social network analysis, and spam detection. However, a fundamental
obstacle in studying SimRank has been the lack of ground truths. The only exact
algorithm, Power Method, is computationally infeasible on graphs with more than
nodes. Consequently, no existing work has evaluated the actual
trade-offs between query time and accuracy on large real-world graphs. In this
paper, we present ExactSim, the first algorithm that computes the exact
single-source and top- SimRank results on large graphs. With high
probability, this algorithm produces ground truths with a rigorous theoretical
guarantee. We conduct extensive experiments on real-world datasets to
demonstrate the efficiency of ExactSim. The results show that ExactSim provides
the ground truth for any single-source SimRank query with a precision up to 7
decimal places within a reasonable query time.Comment: ACM SIGMOD 202
Reverse Nearest Neighbor Heat Maps: A Tool for Influence Exploration
We study the problem of constructing a reverse nearest neighbor (RNN) heat
map by finding the RNN set of every point in a two-dimensional space. Based on
the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the
point. The heat map provides a global view on the influence distribution in the
space, and hence supports exploratory analyses in many applications such as
marketing and resource management. To construct such a heat map, we first
reduce it to a problem called Region Coloring (RC), which divides the space
into disjoint regions within which all the points have the same RNN set. We
then propose a novel algorithm named CREST that efficiently solves the RC
problem by labeling each region with the heat value of its containing points.
In CREST, we propose innovative techniques to avoid processing expensive RNN
queries and greatly reduce the number of region labeling operations. We perform
detailed analyses on the complexity of CREST and lower bounds of the RC
problem, and prove that CREST is asymptotically optimal in the worst case.
Extensive experiments with both real and synthetic data sets demonstrate that
CREST outperforms alternative algorithms by several orders of magnitude.Comment: Accepted to appear in ICDE 201
PRSim: Sublinear Time SimRank Computation on Large Power-Law Graphs
{\it SimRank} is a classic measure of the similarities of nodes in a graph.
Given a node in graph , a {\em single-source SimRank query}
returns the SimRank similarities between node and each node . This type of queries has numerous applications in web search and social
networks analysis, such as link prediction, web mining, and spam detection.
Existing methods for single-source SimRank queries, however, incur query cost
at least linear to the number of nodes , which renders them inapplicable for
real-time and interactive analysis.
{ This paper proposes \prsim, an algorithm that exploits the structure of
graphs to efficiently answer single-source SimRank queries. \prsim uses an
index of size , where is the number of edges in the graph, and
guarantees a query time that depends on the {\em reverse PageRank} distribution
of the input graph. In particular, we prove that \prsim runs in sub-linear time
if the degree distribution of the input graph follows the power-law
distribution, a property possessed by many real-world graphs. Based on the
theoretical analysis, we show that the empirical query time of all existing
SimRank algorithms also depends on the reverse PageRank distribution of the
graph.} Finally, we present the first experimental study that evaluates the
absolute errors of various SimRank algorithms on large graphs, and we show that
\prsim outperforms the state of the art in terms of query time, accuracy, index
size, and scalability.Comment: ACM SIGMOD 201
Dual Nickel/Photoredox-Catalyzed Asymmetric Carbosulfonylation of Alkenes
An asymmetricthree-componentcarbosulfonylationof alkenesis presentedhere. The reaction,involvingthesimultaneousformationof a C−C and a C−S bond acrosstheπ-system,uses a dual nickel/photoredoxcatalyticsystemto producebothβ-aryl andβ-alkenylsulfonesin high yields and with excellentlevels of stereocontrol(up to 99:1 er). This protocolexhibitsabroadsubstratescope and excellentfunctionalgrouptoleranceand its syntheticpotentialhas been demonstratedby successfulapplicationstowardpharmacologicallyrelevantmolecules.A broadarray of controlexperimentssupportsthe involvementof asecondaryalkyl radicalintermediategeneratedthroughradicaladditionof a sulfonylradicalto the doublebond.Moreover,stoichiometricand cross-overexperimentsfurthersuggestan underlyingNi(0)/Ni(I)/Ni(III)pathwayoperativein thesetransformations
Scene-Aware Feature Matching
Current feature matching methods focus on point-level matching, pursuing
better representation learning of individual features, but lacking further
understanding of the scene. This results in significant performance degradation
when handling challenging scenes such as scenes with large viewpoint and
illumination changes. To tackle this problem, we propose a novel model named
SAM, which applies attentional grouping to guide Scene-Aware feature Matching.
SAM handles multi-level features, i.e., image tokens and group tokens, with
attention layers, and groups the image tokens with the proposed token grouping
module. Our model can be trained by ground-truth matches only and produce
reasonable grouping results. With the sense-aware grouping guidance, SAM is not
only more accurate and robust but also more interpretable than conventional
feature matching models. Sufficient experiments on various applications,
including homography estimation, pose estimation, and image matching,
demonstrate that our model achieves state-of-the-art performance.Comment: Accepted to ICCV 202
ParaFormer: Parallel Attention Transformer for Efficient Feature Matching
Heavy computation is a bottleneck limiting deep-learningbased feature
matching algorithms to be applied in many realtime applications. However,
existing lightweight networks optimized for Euclidean data cannot address
classical feature matching tasks, since sparse keypoint based descriptors are
expected to be matched. This paper tackles this problem and proposes two
concepts: 1) a novel parallel attention model entitled ParaFormer and 2) a
graph based U-Net architecture with attentional pooling. First, ParaFormer
fuses features and keypoint positions through the concept of amplitude and
phase, and integrates self- and cross-attention in a parallel manner which
achieves a win-win performance in terms of accuracy and efficiency. Second,
with U-Net architecture and proposed attentional pooling, the ParaFormer-U
variant significantly reduces computational complexity, and minimize
performance loss caused by downsampling. Sufficient experiments on various
applications, including homography estimation, pose estimation, and image
matching, demonstrate that ParaFormer achieves state-of-the-art performance
while maintaining high efficiency. The efficient ParaFormer-U variant achieves
comparable performance with less than 50% FLOPs of the existing attention-based
models.Comment: Have been accepted by AAAI 202
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