859 research outputs found
Link Graph Analysis for Adult Images Classification
In order to protect an image search engine's users from undesirable results
adult images' classifier should be built. The information about links from
websites to images is employed to create such a classifier. These links are
represented as a bipartite website-image graph. Each vertex is equipped with
scores of adultness and decentness. The scores for image vertexes are
initialized with zero, those for website vertexes are initialized according to
a text-based website classifier. An iterative algorithm that propagates scores
within a website-image graph is described. The scores obtained are used to
classify images by choosing an appropriate threshold. The experiments on
Internet-scale data have shown that the algorithm under consideration increases
classification recall by 17% in comparison with a simple algorithm which
classifies an image as adult if it is connected with at least one adult site
(at the same precision level).Comment: 7 pages. Young Scientists Conference, 4th Russian Summer School in
Information Retrieva
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Hypergraph Learning with Line Expansion
Previous hypergraph expansions are solely carried out on either vertex level
or hyperedge level, thereby missing the symmetric nature of data co-occurrence,
and resulting in information loss. To address the problem, this paper treats
vertices and hyperedges equally and proposes a new hypergraph formulation named
the \emph{line expansion (LE)} for hypergraphs learning. The new expansion
bijectively induces a homogeneous structure from the hypergraph by treating
vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple
graph, the proposed \emph{line expansion} makes existing graph learning
algorithms compatible with the higher-order structure and has been proven as a
unifying framework for various hypergraph expansions. We evaluate the proposed
line expansion on five hypergraph datasets, the results show that our method
beats SOTA baselines by a significant margin
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