Community search has become especially important in graph analysis task, which aims to identify latent members of a particular community from few given nodes. Most the existing efforts focus on exploring structure with single scale nodes are located. Despite promising results, following two insights often neglected. First, node attributes provide rich and highly related auxiliary information ap...

Greedy local search search methods have a long history in the area of com-binatorial optimization. One of the earliest application of local search was to nd good solutions for the Traveling Salesman Problem (TSP). In this problem, one is given a weighted graph and the goal is to nd the shortest closed path that visits each node exactly once. The TSP is prototypical of the large class of NP-hard...

This paper outlines an experiential approach to teaching stochastic local search. Using the Tale of the Drunken Topographer as a running analogy, students are led from the implementation of a hill descent algorithm through small, motivated modifications to a simple implementation of simulated annealing. Supplementary applets allow students to experiment with temperature and gain understanding o...

In general, neural networks are regarded as models for massively parallel computation. But very often, this parallelism is rather limited, especially when considering symmetric networks. For instance, Hoppeld networks do not really compute in parallel as their updating algorithm always requires sequential execution. We describe a recurrent network corresponding to a symmetric network and introd...

A novel technique for empirical optimisation is presented, called Dynamic Local Search Algorithm (DLS). The search algorithm starts exploring the solution space only along one of its dimensions at any one time. This is done by perturbing this variable randomly along opposite directions of that dimension, creating two more variables. The magnitude of the perturbation is designed to explore local...