2,825 research outputs found
Structural Deep Embedding for Hyper-Networks
Network embedding has recently attracted lots of attentions in data mining.
Existing network embedding methods mainly focus on networks with pairwise
relationships. In real world, however, the relationships among data points
could go beyond pairwise, i.e., three or more objects are involved in each
relationship represented by a hyperedge, thus forming hyper-networks. These
hyper-networks pose great challenges to existing network embedding methods when
the hyperedges are indecomposable, that is to say, any subset of nodes in a
hyperedge cannot form another hyperedge. These indecomposable hyperedges are
especially common in heterogeneous networks. In this paper, we propose a novel
Deep Hyper-Network Embedding (DHNE) model to embed hyper-networks with
indecomposable hyperedges. More specifically, we theoretically prove that any
linear similarity metric in embedding space commonly used in existing methods
cannot maintain the indecomposibility property in hyper-networks, and thus
propose a new deep model to realize a non-linear tuplewise similarity function
while preserving both local and global proximities in the formed embedding
space. We conduct extensive experiments on four different types of
hyper-networks, including a GPS network, an online social network, a drug
network and a semantic network. The empirical results demonstrate that our
method can significantly and consistently outperform the state-of-the-art
algorithms.Comment: Accepted by AAAI 1
Fairness in online vehicle-cargo matching: An intuitionistic fuzzy set theory and tripartite evolutionary game approach
This paper explores the concept of fairness and equitable matching in an
on-line vehicle-cargo matching setting, addressing the varying degrees of
satisfaction experienced by shippers and carriers. Relevant indicators for
shippers and carriers in the on-line matching process are categorized as
attributes, expectations, and reliability, which are subsequent quantified to
form satisfaction indicators. Employing the intuitionistic fuzzy set theory, we
devise a transformed vehicle-cargo matching optimization model by combining the
fuzzy set's membership, non-membership, and uncertainty information. Through an
adaptive interactive algorithm, the matching scheme with fairness concerns is
solved using CPLEX. The effectiveness of the proposed matching mechanism in
securing high levels of satisfaction is established by comparison with three
benchmark methods. To further investigate the impact of considering fairness in
vehicle-cargo matching, a shipper-carrier-platform tripartite evolutionary game
framework is developed under the waiting response time cost (WRTC) sharing
mechanism. Simulation results show that with fairness concerns in vehicle-cargo
matching, all stakeholders are better off: The platform achieves positive
revenue growth, and shippers and carriers receive positive subsidy. This study
offers both theoretical insights and practical guidance for the long-term and
stable operation of the on-line freight stowage industry.Comment: 36 pages, 15 figure
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