Publications by authors named "Luh Yen"

Article Synopsis
  • The paper conducts a thorough survey and empirical evaluation of seven graph kernels and two related similarity matrices, collectively referred to as "kernels on graphs."
  • It introduces various kernels like the exponential diffusion kernel and the regularized commute-time kernel, examining their effectiveness in collaborative-recommendation tasks and semi-supervised classification on multiple datasets.
  • Results indicate that the regularized commute-time and Markov diffusion kernels excel in performance, with the regularized Laplacian kernel also showing strong results in the evaluated applications.
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This work introduces a link-based covariance measure between the nodes of a weighted directed graph, where a cost is associated with each arc. To this end, a probability distribution on the (usually infinite) countable set of paths through the graph is defined by minimizing the total expected cost between all pairs of nodes while fixing the total relative entropy spread in the graph. This results in a Boltzmann distribution on the set of paths such that long (high-cost) paths occur with a low probability while short (low-cost) paths occur with a high probability.

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This letter addresses the problem of designing the transition probabilities of a finite Markov chain (the policy) in order to minimize the expected cost for reaching a destination node from a source node while maintaining a fixed level of entropy spread throughout the network (the exploration). It is motivated by the following scenario. Suppose you have to route agents through a network in some optimal way, for instance, by minimizing the total travel cost-nothing particular up to now-you could use a standard shortest-path algorithm.

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