Social network search based on semantic analysis and learning

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.

Improving the Syllable-Synchronous Network SearchAlgorithm for Word Decoding in ContinuousChinese Speech Recognition

The previously proposed syllable-synchronous network search (SSNS) algorithm plays a very important role in the word decoding of the continuous Chinese speech recognition and achieves satisfying performance. Several related key factors that may affect the overall word decoding effect are carefully studied in this paper, including the perfecting of the vocabulary, the big-discount Turing re-estimating of the N-Gram probabilities, and the managing of the searching path buffers. Based on these discussions, corresponding approaches to improving the SSNS algorithm are proposed. Compared with the previous version of SSNS algorithm, the new version decreases the Chinese character error rate (CCER) in the word decoding by 42.1% across a database consisting of a large number of testing sentences (syllable strings).

Enhancing the synchronizability of networks by rewiring based on tabu search and a local greedy algorithm

By considering the eigenratio of the Laplacian matrix as the synchronizability measure, this paper presents an efficient method to enhance the synchronizability of undirected and unweighted networks via rewiring. The rewiring method combines the use of tabu search and a local greedy algorithm so that an effective search of solutions can be achieved. As demonstrated in the simulation results, the performance of the proposed approach outperforms the existing methods for a large variety of initial networks, both in terms of speed and quality of solutions.

Structural reliability analysis using enhanced cuckoo search algorithm and artificial neural network

The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.

Enhanced Energy Efficient Multipath Routing Protocol for Wireless Sensor Communication Networks Using Cuckoo Search Algorithm

Energy efficient routing is one of the major thrust areas in Wireless Sensor Communication Networks (WSCNs) and it attracts most of the researchers by its valuable applications and various challenges. Wireless sensor networks contain several nodes in its terrain region. Reducing the energy consumption over the WSCN has its significance since the nodes are battery powered. Various research methodologies were proposed by researchers in this area. One of the bio-inspired computing paradigms named Cuckoo search algorithm is used in this research work for finding the energy efficient path and routing is performed. Several performance metrics are taken into account for determining the performance of the proposed routing protocol such as throughput, packet delivery ratio, energy consumption and delay. Simulation is performed using NS2 and the results shows that the proposed routing protocol is better in terms of average throughput, and average energy consumption.

Using Genetic Algorithms to Improve the Search of the Weight Space in Cascade－Correlation Neural Network

In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.

A Personalized Search Model Using Online Social Network Data Based on a Holonic Multiagent System

Personalized search utilizes user preferences to optimize search results,and most existing studies obtain user preferences by analyzing user behaviors in search engines that provide click-through data.However,the behavioral data are noisy because users often clicked some irrelevant documents to find their required information,and the new user cold start issue represents a serious problem,greatly reducing the performance of personalized search.This paper attempts to utilize online social network data to obtain user preferences that can be used to personalize search results,mine the knowledge of user interests,user influence and user relationships from online social networks,and use this knowledge to optimize the results returned by search engines.The proposed model is based on a holonic multiagent system that improves the adaptability and scalability of the model.The experimental results show that utilizing online social network data to implement personalized search is feasible and that online social network data are significant for personalized search.

Channel Assignment Method Using Parallel Tabu Search Based on Graph Theory in Wireless Sensor Networks

Wireless sensor networks are suffering from serious frequency interference.In this paper,we propose a channel assignment algorithm based on graph theory in wireless sensor networks.We first model the conflict infection graph for channel assignment with the goal of global optimization minimizing the total interferences in wireless sensor networks.The channel assignment problem is equivalent to the generalized graph-coloring problem which is a NP-complete problem.We further present a meta-heuristic Wireless Sensor Network Parallel Tabu Search(WSN-PTS) algorithm,which can optimize global networks with small numbers of iterations.The results from a simulation experiment reveal that the novel algorithm can effectively solve the channel assignment problem.

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer （P2P） network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

Greedysearch based service location in P2P networks

A model is built to analyze the performance of service location based on greedy search in P2P networks. Hops and relative QoS index of the node found in a service location process are used to evaluate the performance as well as the probability of locating the top 5% nodes with highest QoS level. Both model and simulation results show that, the performance of greedy search based service location improves significantly with the increase of the average degree of the network. It is found that, if changes of both overlay topology and QoS level of nodes can be ignored during a location process, greedy-search based service location has high probability of finding the nodes with relatively high QoS in small number of hops in a big overlay network. Model extension under arbitrary network degree distribution is also studied.

Research of Rural Power Network Reactive Power Optimization Based on Improved ACOA

In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm （ACOA） was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects： one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county＇s distribution network showed that the improved ACOA was practicable.

Searching maximum quasi-bicliques from protein-protein interaction network

Searching the maximum bicliques or bipartite subgraphs in a graph is a tough question. We proposed a new and efficient method, Searching Quasi-Bicliques (SQB) algorithm, to detect maximum quasi-bicliques from protein-protein interaction network. As a Divide-and-Conquer method, SQB consists of three steps: first, it divides the protein-protein interaction network into a number of Distance-2-Subgraphs;second, by combining top-down and branch-and-bound methods, SQB seeks quasi-bicliques from every Distance-2-Subgraph;third, all the redundant results are removed. We successfully applied our method on the Saccharomyces cerevisiae dataset and obtained 2754 distinct quasi-bicliques.

日本国立国会图书馆新检索服务“NDL Search”介绍及分析

日本国立国会图书馆于2012年1月正式启动了新的检索服务"国立国会图书馆Search(NDLSearch)",本文对该系统的开发背景、检索范围及功能等进行了介绍和分析。

Study on the CAN Search Model based on BDI Agent

The main purpose of establishing a complex agent network （CAN） search model is to specifically model each type of the relationships between different types of Agent structure domain and make it easier to be implemented in the existing programming language environment. Under the guidance of complex Agent network method, CAN search process was analyzed, a dynamic search model description was established based on CAN search process, and then individual Agent modelling and the memory and processing of the thinking attributes such as beliefs, desires and intentions in CAN search process were mainly introduced from the individual level; all sorts of Agent conceptual models and Agent type descriptions for CAN search model were designed by introducing BDI Agent; the states and behaviors of the Agent involving in CAN search process were clearly defined.

A network data-based survey and analysis of attention towards breast reconstruction after breast cancer surgery in Chinese and American populations

Background:Breast reconstruction is an effective technique to rebuild the appearance of the breasts in patients after mastectomy and improves the prognosis.The current study aimed to compare and analyze willingness for breast reconstruction after breast cancer between populations in China and the United States,from the perspective of social concern,using big data analysis.We also aimed to explore factors affecting surgical selection and to identify methods that can improve social cognition and acceptance of breast reconstruction.Methods:Using Baidu and Google,two representative Internet search engines in China and the United States as research tools,and using big data search volume as the benchmark,we compared and analyzed breast reconstruction willingness and attention characteristics between Chinese and American people,based on search heat,geographical distribution,age and sex,keyword distribution,ethnic group,and social development degree.Results:In both the long-term and short-term,Chinese people paid more attention towards searching about breast cancer,but less attention to breast reconstruction after breast cancer surgery.However,in both the short-term and long-term,people from the United States paid more attention towards breast cancer and breast reconstruction with the help of the Internet,showing a synchronous change relationship.There was a large regional difference in the search volume for breast cancer among the Chinese population,while no significant regional differences were noted in the search volume for breast cancer in the United States.However,a large regional difference was observed in the search volume for breast reconstruction between the two countries;people in the coastal and economically developed areas paid more attention to it.Most people who paid attention to breast reconstruction in China were women aged 20–39 years,while the attention among men was low.Search keywords were also limited to breast cancer-related information.However,between Asians and European Americans,Americans paid more attention to breast cancer and were affected by regional development,religious beliefs,and health facilities.Conclusion:Attention towards breast reconstruction after breast cancer was lower in the Chinese population than in the American population,and this difference was closely related to the level of regional development.There is insufficient information on breast reconstruction after breast cancer in recent Internet media.In addition to strengthening communication in clinics,media education is important to improve the cognitive level and social awareness of patients and their families,which is conducive to breast reconstruction.

基于Beetle Antennae Search算法主动配电网经济调度

为提高主动配电网的经济效益,在考虑分布式能源、网损费用和发电成本基础上,建立了主动配电网经济调度模型。模型加入了柔性软开关对主动配电网进行灵活调度和无功优化,使用优化后天牛须搜索算法对其进行求解,优化方法为在算法中加入遗传因子和锦标赛选择策略,使算法结果不会陷入局部最优,得到最小发电成本。最后经仿真实验,验证了优化后的天牛须搜索算法在主动配电网经济调度问题中具有实用性和高效性。

Structure learning on Bayesian networks by finding the optimal ordering with and without priors

Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.

Navigation on Power-Law Small World Network with Incomplete Information

We investigate the navigation process on a variant of the Watts-Strogatz small-world network model with local information. In the network construction, each vertex of an N x N square lattice sends out a long-range link with probability p. The other end of the link falls on a randomly chosen vertex with probability proportional to r^-α, where r is the lattice distance between the two vertices, and α ≥ 0. The average actual path length, i.e. the expected number of steps for passing messages between randomly chosen vertex pairs, is found to scale as a power-law function of the network size N^β, except when α is close to a specific value value, which gives the highest efficiency of message navigation. For a finite network, the exponent β depends on both α and p, and p αmin drops to zero at a critical value of p which depends on N. When the network size goes to infinity,β depends only only on α, and αmin is equal to the network dimensionality.

神经架构搜索综述

近几年,深度学习因具有强大的表征能力,已经在许多领域中取得了突破性的进展,而神经网络的架构对它的性能至关重要。然而,高性能的神经网络架构设计严重依赖研究人员的先验知识和经验,神经网络参数量庞大,难以设计最优的神经网络架构,因此自动神经架构搜索(NAS)获得了极大的关注。NAS是一种使用机器学习的方法,可以在不需要大量人力的情况下,自动搜索最优网络架构的技术,是未来神经网络设计的重要手段之一。NAS本质上是一个搜索优化问题,通过对搜索空间、搜索策略和性能评估策略的设计,自动搜索最优的网络结构。从搜索空间、搜索策略和性能评估策略这3个方面详细且全面地分析、比较和总结目前NAS的研究进展,方便读者快速了解神经架构搜索的发展过程和各项技术的优缺点,并提出NAS未来可能的研究发展方向。