Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vision, and natural language processing. With the increasing size of datasets in these domains, there is a pressing need to develop efficient and parallelizable algorithms for submodular maximization. One measure of the parallelizability of a submodular maximization algorithm is its adaptive complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose a low-adaptivity algorithm achieving an (1/8 − ϵ)- approximation with practical Õ(n) query complexity. Moreover, we also propose the first algorithm with both provable approximation ratio and sublinear adaptive complexity for the problem of non-monotone submodular maximization subject to a k-system constraint. As a by-product, we show that our two algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our algorithms is demonstrated by extensive experiments on real-world applications.

Submodular maximization has wide applications in machine learning and data mining, where massive datasets have brought the great need for designing efficient and parallelizable algorithms. One measure of the parallelizability of a submodular maximization algorithm is its adaptivity complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose the first combinatorial algorithm achieving an (8+epsilon)-approximation under O(log n) adaptive complexity, which is optimal up to a factor of O(loglog n). Moreover, under slightly larger adaptivity, we also propose approximation algorithms with nearly optimal query complexity of O(n), while achieving better approximation ratios. We show that our algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our approach is demonstrated by extensive experiments on real-world applications.

Enterprise competition analysis has long been treated as a crucial task of management science, which can reveal pertinent information about market saturation and business opportunities to support the decision-making process of entrepreneurs and investors. Recently, with the development of graph representation techniques, enterprises could be now formulated in a novel graph-oriented perspective to model their cooperation and competition in a directional graph. However, these prior arts mainly treat the enterprises as individual nodes, while the ecosystems, i.e., enterprise groups formed based on their common interests, and consequent interaction effects have been largely ignored. To address this issue, in this article, by adapting the concept of hypergraph to describe enterprise ecosystems, we propose a novel multiview enterprise relation network (MERN) framework for enterprise competition analysis. Specifically, we first put forward a hypertranslating embedding (HTransE) algorithm inspired by translation distance models for the hyperrelation embedding of ecosystems. Meanwhile, a relational graph convolutional network (RGCN) is designed for ordinary relation embedding, e.g., supplier, client, or competitor. Afterward, considering the commercial diversity in ecosystems, which means that different enterprises may focus on different industry fields, we propose an industry trend embedding module to describe industry distribution and development trend factors for each enterprise. Finally, we apply the self-attention mechanism to integrate these three modules and adapt the bilinear model DistMult for competition link analysis. Extensive experiments on a real-world dataset validate that our solution can achieve better performance compared with several competitive baselines.

Given the edge list of a social network, the node embedding method learns the structural features for every node and embeds the features into a vector space. The current related work on node embedding exploits only a portion of existing networks, e.g., static networks. However, social networks are inherently hierarchical and dynamic systems in which the topology changes constantly and the strength of influence of information among neighbors varies with different numbers of hops. We propose a highly efficient node embedding method, DNPS, that is faster and more accurate than state-of-the-art methods and that can further boost the training progress, especially under dynamic conditions. In this paper, we attempt to model the hierarchical and dynamic features of social networks by designing a damping-based sampling algorithm corresponding to a local search-based incremental learning algorithm, which can easily be extended to large-scale scenarios. We conduct extensive experiments on six real-world social networks with three challenging tasks, including missing link prediction, dynamic link prediction, and multi-label classification. The results of the experiments on these tasks demonstrate that the proposed method significantly outperforms the existing methods with different settings.

This paper studies the problem of learning node embeddings (a.k.a. distributed representations) for dynamic networks. The embedding methods allocate each node in network with a d-dimensions vector, which can generalize across various tasks, such as item recommendation, node labeling, and link prediction. In practice, many real-world networks are evolving with nodes/links added or deleted. However, most of the proposed methods are focusing on static networks. Although some previous researches have shown some promising results to handle the dynamic scenario, they just considered the added links and ignored the deleted ones. In this work, we designed a joint learning of added and deleted links model, named RDEM, for dynamic network embedding.

Learning distributed representations of symbolic data were introduced by Hinton[1], and first developed in modeling networks for learning the node vectors by Perozzi et al (2014). In this work, we proposed Dnps, a novel nodes embedding approach for acquiring distributed representations of large-scale dynamic social networks. Dnps is suitable for many types of social networks: dynamic/static, directed/undirected, and weighted/unweighted. Recently, several works of nodes embedding were proposed. However, they were designed for static networks, such as language networks. To address this problem, first, we develop a damping based positive sampling (DpS) algorithm to learn the hierarchical structure of social networks. Then, we devise a local search based DpS algorithm to obtain incremental information of network evolution. Finally, we show Dnps's potentials on future link prediction task for three real-life large-scale dynamic social networks. The results show that Dnps consistently outperforms all baseline methods and exhibits an improvement of 12%, 6%, 4% on Digg, Flickr and YouTube over the second-highest level, respectively. Moreover, Dnps is also scalable. For example, Dnps can speed up the training process in 2 ~ 36 times compared with benchmarks on Flickr network. The source codes of the project is available online1.

We present an algorithm (LsNet2Vec) that, given a large-scale network (millions of nodes), embeds the structural features of node into a lower and fixed dimensions of vector in the set of real numbers. We experiment and evaluate our proposed approach with twelve datasets collected from SNAP. Results show that our model performs comparably with state-of-the-art methods, such as Katz method and Random Walk Restart method, in various experiment settings.

In this paper, we present Cdear, a novel system to detect fake reviews by using sentiment analysis on attributes of products. We formulate review spam detection as an opinion coincided problem. Specifically, we try to capture the sentiment diverse of attributes of products among different consumers. To our knowledge, this is the very first attempt to use sentiment analysis on attributes of products in reviews to detect review spam. To evaluate the effectiveness of our system, we conduct the experiments on the real life datasets and employ 20 experts to assess the reliability of reviews by carrying out a simulation of shopping online. Meanwhile, we developed an evaluation system to help those experts to assess the reviews, thus to ensure the scientificity of experiments. Comparison between the automatic results of proposed system and human evaluated results demonstrates that the sentiment-based method matches approximately with human perception of reviews' reliability with 82.66% accuracy.