114 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
Gauging Correct Relative Rankings For Similarity Search
© 2015 ACM.One of the important tasks in link analysis is to quantify the similarity between two objects based on hyperlink structure. SimRank is an attractive similarity measure of this type. Existing work mainly focuses on absolute SimRank scores, and often harnesses an iterative paradigm to compute them. While these iterative scores converge to exact ones with the increasing number of iterations, it is still notoriously difficult to determine how well the relative orders of these iterative scores can be preserved for a given iteration. In this paper, we propose efficient ranking criteria that can secure correct relative orders of node-pairs with respect to SimRank scores when they are computed in an iterative fashion. Moreover, we show the superiority of our criteria in harvesting top-K SimRank scores and bucket orders from a full ranking list. Finally, viable empirical studies verify the usefulness of our techniques for SimRank top-K ranking and bucket ordering
Representation Independent Analytics Over Structured Data
Database analytics algorithms leverage quantifiable structural properties of
the data to predict interesting concepts and relationships. The same
information, however, can be represented using many different structures and
the structural properties observed over particular representations do not
necessarily hold for alternative structures. Thus, there is no guarantee that
current database analytics algorithms will still provide the correct insights,
no matter what structures are chosen to organize the database. Because these
algorithms tend to be highly effective over some choices of structure, such as
that of the databases used to validate them, but not so effective with others,
database analytics has largely remained the province of experts who can find
the desired forms for these algorithms. We argue that in order to make database
analytics usable, we should use or develop algorithms that are effective over a
wide range of choices of structural organizations. We introduce the notion of
representation independence, study its fundamental properties for a wide range
of data analytics algorithms, and empirically analyze the amount of
representation independence of some popular database analytics algorithms. Our
results indicate that most algorithms are not generally representation
independent and find the characteristics of more representation independent
heuristics under certain representational shifts
A Framework for Comparing Groups of Documents
We present a general framework for comparing multiple groups of documents. A
bipartite graph model is proposed where document groups are represented as one
node set and the comparison criteria are represented as the other node set.
Using this model, we present basic algorithms to extract insights into
similarities and differences among the document groups. Finally, we demonstrate
the versatility of our framework through an analysis of NSF funding programs
for basic research.Comment: 6 pages; 2015 Conference on Empirical Methods in Natural Language
Processing (EMNLP '15
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
Efficient PartialPairs SimRank search on large graphs
The assessment of node-to-node similarities based on graph topology arises in a myriad of applications, e.g., web search. SimRank is a notable measure of this type, with the intuition that âtwo nodes are similar if their in-neighbors are similarâ. While most existing work retrieving SimRank only considers all-pairs SimRank s(â, â) and single-source SimRank s(â, j) (scores between every node and query j), there are appealing applications for partial-pairs SimRank, e.g., similarity join. Given two node subsets A and B in a graph, partial-pairs SimRank assessment aims to retrieve only {s(a, b)}âaâA,âbâB. However, the best-known solution [17] is not self-contained since it hinges on the premise that the SimRank scores with node-pairs in an h-go cover set must be given beforehand. This paper focuses on efficient assessment of partial-pairs SimRank in a self-contained manner. (1) We devise a novel âseed germinationâ model that computes partial-pairs Sim- Rank in O(k|E|min{|A|, |B|}) time and O(|E|+k|V |) memory for k iterations on a graph of |V | nodes and |E| edges. (2) We further eliminate unnecessary edge access to improve the time of partial-pairs SimRank to O(mmin{|A|, |B|}), where m †min{k|E|, 2k}, and is the maximum degree. (3) We show that our partial-pairs SimRank model also can handle the computations of all-pairs and single-source Sim- Ranks, as well as partial-pairs SimRank* (a related notion of SimRank). (4) We empirically verify that our algorithms are (a) 38x faster than the best-known competitors, and (b) memory-efficient, allowing scores to be assessed accurately on graphs with tens of millions of links
Efficient and Effective Similarity Search over Bipartite Graphs
Similarity search over a bipartite graph aims to retrieve from the graph the
nodes that are similar to each other, which finds applications in various
fields such as online advertising, recommender systems etc. Existing similarity
measures either (i) overlook the unique properties of bipartite graphs, or (ii)
fail to capture high-order information between nodes accurately, leading to
suboptimal result quality. Recently, Hidden Personalized PageRank (HPP) is
applied to this problem and found to be more effective compared with prior
similarity measures. However, existing solutions for HPP computation incur
significant computational costs, rendering it inefficient especially on large
graphs.
In this paper, we first identify an inherent drawback of HPP and overcome it
by proposing bidirectional HPP (BHPP). Then, we formulate similarity search
over bipartite graphs as the problem of approximate BHPP computation, and
present an efficient solution Approx-BHPP. Specifically, Approx-BHPP offers
rigorous theoretical accuracy guarantees with optimal computational complexity
by combining deterministic graph traversal with matrix operations in an
optimized and non-trivial way. Moreover, our solution achieves significant gain
in practical efficiency due to several carefully-designed optimizations.
Extensive experiments, comparing BHPP against 8 existing similarity measures
over 7 real bipartite graphs, demonstrate the effectiveness of BHPP on query
rewriting and item recommendation. Moreover, Approx-BHPP outperforms baseline
solutions often by up to orders of magnitude in terms of computational time on
both small and large datasets.Comment: Best Paper Award Nominee in WWW 2022. Fixing the incorrect figure
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