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Service recommendation technology is the key to realize the personalization of intelligent services. The recommended services need to meet functional requirements as well as non-functional requirements. Therefore, QoS-based service recommendation came into being. To perform intelligent service recommendations, matching users with convenient services based on QoS becomes an inevitable task. However, most of the service recommendation models are based on user interaction records to predict and recommend, ignoring the service-user correlation and unstable QoS values. In this article, we propose a new service recommendation model. We have performed two-tier filtering calculation on a large number of Web Services, filtering the contextual information of users and services and the instability of services. In the first filtering layer, we take the instability of QoS as an indicator to eliminate invalid services, which significantly reduces the service scale and eliminates the interference of invalid services on the recommendation to a certain extent. Further, we process the contextual information of both users and services in the second filtering layer. Considering the impact of the correlation between the service and the user, we use the geographic location information of the user and the service, and solve the combined features generated by the similarity between the user and the service to filter. Considering the sparsity of the service recommendation environment and the influence of noise generated by useless features, we use a model of factorization machine combined with the attention mechanism for computational processing. It effectively distinguishes the interactive importance of different features. We have conducted many experiments on real dataset, and the results show that our model is better than most baseline model in terms of recommendation performance.

In this paper, we demonstrate the light interaction mechanism of black phosphorus (BP) in two different structures: the photonic crystal waveguide (PCW) and photonic crystal mirror (PCM). The optical characteristics are simulated by the numerical method finite difference time domain. Specifically, the coupled mode theory is used to investigate the light propagation in PCW structure integrated with BP. By optimizing the geometric parameters, an enhanced light absorption in BP with the maximum of 36.8% at 1550 nm could be obtained. Additionally, with the assistance of BP film, the ultracompact PCM used for a 90° bend connection can achieve a high-reflective efficiency at ~94% for transverse electric (TE) mode incidence. It is demonstrated that these interesting structures can offer a new way to develop ultracompact photonic integrations based on two-dimensional materials at near infrared field.

Metaphors are compact packages of information with rich cultural background information. As one of the most powerful linguistic forms with non-literal meaning, metaphor detection in natural language processing can be both challenging and rewarding. We propose an innovative method for metaphor detection and classification leveraging culturally grounded eventive information. This culturally grounded information is organized based on ontological structure, which in turn facilitates further semantic processing of the result of our classification. As a culturally bound ontological system, the Chinese writing system has basic concepts organized according to semantic radicals, which are symbols containing rich eventive information that represent categorical concepts. This paper illustrates the basic design principles of applying ontological structures in metaphor detection by taking into account radicals representing body parts, instruments, materials, and movements. Our approach to leverage the eventive information of the Chinese writing system in metaphor detection is based on the fact that such information is available as an integral part of the writing system of any text. We hypothesize that eventive information can be accessed through the “embodied” source domain information represented by the radicals without syntactic processing or annotation. In terms of the theory of metaphor, we further hypothesize that eventive types in the embodied source domain maps to, and hence can help to predict, eventive meaning in the target domain of metaphor. Our studies show that the event information encoded in lexical items can facilitate classification of metaphoric events and identification of metaphors in Chinese texts effectively. We achieved improvements in Chinese metaphor detection over state-of-the-art approaches in our first classification experiment, and our proposed approach is shown to be generalizable in a second experiment involving new sets of characters with the same radicals.

Finding methods to represent multiple types of nodes in heterogeneous networks is both challenging and rewarding, as there is much less work in this area compared with that of homogeneous networks. In this paper, we propose a novel approach to learn node embedding for heterogeneous networks through a joint learning framework of both network links and text associated with nodes. A novel attention mechanism is also used to make good use of text extended through links to obtain much larger network context. Link embedding is first learned through a random-walk-based method to process multiple types of links. Text embedding is separately learned at both sentence level and document level to capture salient semantic information more comprehensively. Then, both types of embeddings are jointly fed into a hierarchical neural network model to learn node representation through mutual enhancement. The attention mechanism follows linked edges to obtain context of adjacent nodes to extend context for node representation. The evaluation on a link prediction task in a heterogeneous network data set shows that our method outperforms the current state-of-the-art method by 2.5%-5.0% in AUC values with p-value less than 10-9, indicating very significant improvement.

Driven by real-life applications in geo-social networks, we study the problem of computing radius-bounded $k$ k -cores (RB- $k$ k -cores) that aims to find communities satisfying both social and spatial constraints. In particular, the model $k$ k -core (i.e., the subgraph where each vertex has at least $k$ k neighbors) is used to ensure the social cohesiveness, and a radius-bounded circle is used to restrict the locations of users in an RB- $k$ k -core. We explore several algorithmic paradigms to compute RB- $k$ k -cores, including a triple-vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating-circle-based paradigm is further enhanced by several pruning techniques to achieve better efficiency. In addition, to find representative RB- $k$ k -cores, we study the diversified radius-bounded $k$ k -core search problem, which finds $t$ t RB- $k$ k -cores to cover the most number of vertices. We first propose a baseline algorithm that identifies the distinctive RB- $k$ k -cores after finding all the RB- $k$ k -cores. Beyond this, we design algorithms that can efficiently maintain the top- $t$ t candidate RB- $k$ k -cores and also achieve a guaranteed approximation ratio. Experimental studies on both real and synthetic datasets demonstrate that our proposed techniques can efficiently compute (diversified) RB- $k$ k -cores. Moreover, our techniques can be used to compute the minimum-circle-bounded $k$ k -core and significantly outperform the existing techniques. [ABSTRACT FROM AUTHOR]

Periodicity is a frequently happening phenomenon for social interactions in temporal networks. Mining periodic communities are essential to understanding periodic group behaviors in temporal networks. Unfortunately, most previous studies for community mining in temporal networks ignore the periodic patterns of communities. In this paper, we study the problem of seeking periodic communities in a temporal network, where each edge is associated with a set of timestamps. We propose novel models, including $\sigma$ σ -periodic $k$ k -core and $\sigma$ σ -periodic $k$ k -clique, that represent periodic communities in temporal networks. Specifically, a $\sigma$ σ -periodic $k$ k -core (or $\sigma$ σ -periodic $k$ k -clique) is a $k$ k -core (or clique with size larger than $k$ k ) that appears at least $\sigma$ σ times periodically in the temporal graph. The problem of searching periodic core is efficient but the resulting communities may be not enough cohesive; the problem of enumerating all periodic cliques is not efficient (NP-hard) but the resulting communities are very cohesive. To compute all of them efficiently, we first develop two effective graph reduction techniques to significantly prune the temporal graph. Then, we transform the temporal graph into a static graph and prove that mining the periodic communities in the temporal graph equals mining communities in the transformed graph. Subsequently, we propose a decomposition algorithm to search maximal $\sigma$ σ -periodic $k$ k -core, a Bron-Kerbosch style algorithm to enumerate all maximal $\sigma$ σ -periodic $k$ k -cliques, and a branch-and-bound style algorithm to find the maximum $\sigma$ σ -periodic clique. The results of extensive experiments on five real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

Uncertain graph management and analysis have attracted many research attentions. Among them, computing $k$ k -cores in uncertain graphs (aka, $(k,\eta)$ (k , η) -cores) is an important problem and has emerged in many applications such as community detection, protein-protein interaction network analysis and influence maximization. Given an uncertain graph, the $(k,\eta)$ (k , η) -cores can be derived by iteratively removing the vertex with an $\eta$ η -degree of less than $k$ k . However, the results heavily depend on the two input parameters $k$ k and $\eta$ η . The settings for these parameters are unique to the specific graph structure and the user's subjective requirements. In addition, computing and updating the $\eta$ η -degree for each vertex is the most costly component in the algorithm, and the cost is high. To overcome these drawbacks, we propose an index-based solution for computing $(k,\eta)$ (k , η) -cores. The size of the index is well bounded by $O(m)$ O (m) , where $m$ m is the number of edges in the graph. Based on the index, queries for any $k$ k and $\eta$ η can be answered in optimal time. We propose an algorithm for index construction with several different optimizations. We also propose a new algorithm for index construction in external memory. We conduct extensive experiments on eight real-world datasets to practically evaluate the performance of all proposed algorithms. [ABSTRACT FROM AUTHOR]

Degeneracy is an important concept to measure the sparsity of a graph which has been widely used in many network analysis applications. Many network analysis algorithms, such as clique enumeration and truss decomposition, perform very well in graphs having small degeneracies. In this paper, we propose an I/O-efficient algorithm to compute the degeneracy of the massive graph that cannot be fully kept in the main memory. The proposed algorithm only uses $O(n)$ O (n) memory, where $n$ n denotes the number of nodes of the graph. We also develop an I/O-efficient algorithm to incrementally maintain the degeneracy on dynamic graphs. Extensive experiments show that our algorithms significantly outperform the state-of-the-art degeneracy computation algorithms in terms of both running time and I/O costs. The results also demonstrate high scalability of the proposed algorithms. For example, in a real-world web graph with 930 million nodes and 13.3 billion edges, the proposed algorithm takes only 633 seconds and uses less than 4.5GB memory to compute the degeneracy. [ABSTRACT FROM AUTHOR]