1,122 research outputs found
A Multiscale Pyramid Transform for Graph Signals
Multiscale transforms designed to process analog and discrete-time signals
and images cannot be directly applied to analyze high-dimensional data residing
on the vertices of a weighted graph, as they do not capture the intrinsic
geometric structure of the underlying graph data domain. In this paper, we
adapt the Laplacian pyramid transform for signals on Euclidean domains so that
it can be used to analyze high-dimensional data residing on the vertices of a
weighted graph. Our approach is to study existing methods and develop new
methods for the four fundamental operations of graph downsampling, graph
reduction, and filtering and interpolation of signals on graphs. Equipped with
appropriate notions of these operations, we leverage the basic multiscale
constructs and intuitions from classical signal processing to generate a
transform that yields both a multiresolution of graphs and an associated
multiresolution of a graph signal on the underlying sequence of graphs.Comment: 16 pages, 13 figure
A Network Resource Allocation Recommendation Method with An Improved Similarity Measure
Recommender systems have been acknowledged as efficacious tools for managing
information overload. Nevertheless, conventional algorithms adopted in such
systems primarily emphasize precise recommendations and, consequently, overlook
other vital aspects like the coverage, diversity, and novelty of items. This
approach results in less exposure for long-tail items. In this paper, to
personalize the recommendations and allocate recommendation resources more
purposively, a method named PIM+RA is proposed. This method utilizes a
bipartite network that incorporates self-connecting edges and weights.
Furthermore, an improved Pearson correlation coefficient is employed for better
redistribution. The evaluation of PIM+RA demonstrates a significant enhancement
not only in accuracy but also in coverage, diversity, and novelty of the
recommendation. It leads to a better balance in recommendation frequency by
providing effective exposure to long-tail items, while allowing customized
parameters to adjust the recommendation list bias
Locally Optimal Load Balancing
This work studies distributed algorithms for locally optimal load-balancing:
We are given a graph of maximum degree , and each node has up to
units of load. The task is to distribute the load more evenly so that the loads
of adjacent nodes differ by at most .
If the graph is a path (), it is easy to solve the fractional
version of the problem in communication rounds, independently of the
number of nodes. We show that this is tight, and we show that it is possible to
solve also the discrete version of the problem in rounds in paths.
For the general case (), we show that fractional load balancing
can be solved in rounds and discrete load
balancing in rounds for some function , independently of the
number of nodes.Comment: 19 pages, 11 figure
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