In online advertising market, advertisers aim to target the right audience through media channels such as display, mobile, video, or social with limited budget. Usually the advertiser can collect high valuable users from their existing customs, whose size however is usually small. Hence, it is very important for advertisers to reach more audiences who can potentially be turned into future customers. How to induce the preferable attributes from the seed audience and hence identify more similar users from billions of cookie-able users within the runtime of a few minutes is a challenge to data science techniques. This is an important problem and many researchers have proposed several solutions [1]-[6] In this paper, we propose an efficient approach which can return a set of Boolean rules that defines the extended audience by analyzing the seed audience. As an additional feature, the proposed system also provides an option for advertisers to participate in the decision making process which could potentially improve the user reachability as well as the campaign performance. The experiment results show the superior performance of the proposed system compared with other methods in terms of efficiency, user reachability, action rate, click through rate, etc.

Traditional network classification techniques will become computationally intractable when applied on a network which is presented in a streaming fashion with continuous updates. In this paper, we examine the problem of classification in dynamic streaming networks, or graphs. Two scenarios have been considered: the graph transaction scenario and the one large graph scenario. We propose a unified framework consisting of three components: a subgraph extraction method, an online version of an existing graph kernel, and two kernel-based incremental learners. We demonstrate the advantages of our framework via empirical evaluations on several real-world network datasets.

With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective kernels to compute similarities for static graphs. However, they become computationally intractable in terms of t ime and space when a graph is presented in an incremental fashion with continuous updates, i.e., insertions of nodes and edges. In this paper, we examine the problem of classification in large-scale and incrementally changing graphs. We propose a framework combining an incremental support vector machine (SVM) with the Weisfeiler-Lehman (W-L) graph kernel. By retaining the support vectors from each learning step, the classification model is incrementally updated whenever new changes are made to the graph. We design an entropy-based subgraph extraction strategy, that selects informative neighbor nodes and discards those with less discriminative power, to facilitate the classification of nodes in a dynamic network. We validate the advantages of our learning techniques by conducting an empirical evaluation on several large-scale real-world graph datasets in comparison with other graph classification methods. The experimental results also validate the benefits of our subgraph extraction method when combined with the incremental learning techniques.

We examine the problem of node classification in large-scale and dynamically changing graphs. An entropy-based subgraph extraction method has been developed for extracting subgraphs surrounding the nodes to be classified. We introduce an online version of an existing graph kernel to incrementally compute the kernel matrix for a unbounded stream of these extracted subgraphs. After obtaining the kernel values, we adopt a kernel perceptron to learn a discriminative classifier and predict the class labels of the target nodes with their corresponding subgraphs. We demonstrate the advantages of our learning techniques by conducting empirical evaluations on two real-world graph datasets.

With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective kernels to compute similarities for static graphs. However, they become computationally intractable in terms of time and space when a graph is presented in a incremental fashion with continuous updates, i.e., Insertions of nodes and edges. In this paper, we examine the problem of classification in large-scale and incrementally changing graphs. To this end, a framework combining an incremental Support Vector Machine (SVM) with the Weisfeiler-Lehman (W-L) graph kernel has been proposed to study this problem. By retaining the support vectors from each learning step, the classification model is incrementally updated whenever new changes are made to the subject graph. Furthermore, we design an entropy-based sub graph extraction strategy to select informative neighbor nodes and discard those with less discriminative power, to facilitate an effective classification process. We demonstrate the advantages of our learning techniques by conducting an empirical evaluation on two large-scale real-world graph datasets. The experimental results also validate the benefits of our sub graph extraction method when combined with the incremental learning techniques.