Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core, which can cap...

Efficient processing of top-k queries has become a classical research area. Fagin et al. proposed the “middleware cost” for a top-k query algorithm. In some scenario, there is no way to perform a random access, and Fagin et al. proposed NRA (No Random Access) algorithm for that. In this paper, we investigate the intrinsic relation between top-k queries and K-skyband queries. Based on that relat...

Given a user-specified minimum correlation threshold and a transaction database, the problem of mining strongly correlated item pairs is to find all item pairs with Pearson's correlation coefficients above the threshold. However, setting such a threshold is by no means an easy task. In this paper, we consider a more practical problem: mining top-k strongly correlated item pairs, where k is the ...

Information streams provide today a prevalent way of publishing and consuming content on the Web, especially due to the great success of social networks. Top-k queries over the streams of interest allow limiting results to the most relevant content, while continuous processing of such queries is the most effective approach in large scale systems. However, current systems fail in combining conti...

Ranking techniques have been widely used in ubiquitous applications like recommendation, information retrieval, etc. For ranking computation hostile but human friendly items, crowdsourcing is considered as an emerging technique to process the ranking by human power. However, there is a lack of an easy-to-use toolkit for answering crowdsourced top-k query with minimal effort. In this work, we de...

In this project I explored the Top Leaders algorithm [1], and compared it with several other community discovery algorithms. Community discovery is an important and interesting research field in the analytics of social network. By detecting communities in a social network, companies can adopt different marketing strategies and recommend different products for people in different communities, or...

In an uncertain data set S = (S, p, f) where S is the ground set consisting of n elements, p : S → [0, 1] a probability function, and f : S → R a score function, each element i ∈ S with score f(i) appears independently with probability p(i). The top-k query on S asks for the set of k elements that has the maximum probability of appearing to be the k elements with the highest scores in a random ...

Driven by many real applications, graph pattern matching has attracted a great deal of attention recently. Consider that a twigpattern matching may result in an extremely large number of matches in a graph; this may not only confuse users by providing too many results but also lead to high computational costs. In this paper, we study the problem of top-k tree pattern matching; that is, given a ...

The top-k error is a common measure of performance in machine learning and computer vision. In practice, top-k classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objective for such a task in the limit of infinite data. In the context of limited and noisy data however, t...

In this work we study the mining of top-K frequent closed itemsets, a recently proposed variant of the classical problem of mining frequent closed itemsets where the support threshold is chosen as the maximum value sufficient to guarantee that the itemsets returned in output be at least K. We discuss the effectiveness of parameter K in controlling the output size and develop an efficient algori...