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.

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.

Optimization of satellite searching strategy of the non-stationary antenna

The tiny searching step length and the satellite distribution density are the major factors to influence the efficiency of the satellite finder,so a scientific and reasonable method to calculate the tiny searching step length is proposed to optimize the satellite searching strategy. The pattern clustering and BP neural network are applied to optimize the tiny searching step length. The calculated tiny searching step length is approximately equal to the theoretic value for each satellite. In application,the satellite searching results will be dynamically added to the training samples to re-train the network to improve the generalizability and the precision. Experiments validate that the optimization of the tiny searching step length can avoid the error of locating target satellite and improve the searching efficiency.

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.

Evolution of China's Role in the Structure of Global Carbon Emission Transfers:An Empirical Analysis Based on Network Governance

This paper reconsiders the roles of China and some developed countries in the network of carbon emission transfers via international trade in value added from a new perspective of network governance.Network search intensity(NSI)and the extended gravity model are used with cross-country panel data to analyze the mechanism of China's engagement in network governance of carbon emission transfers.The results show that from 2000 to 2009,China was a net exporter of carbon emissions,even though it shifted from the semi-periphery to the core in the network of carbon emissions embodied in imports.Meanwhile,NSI had a significant positive impact on carbon emissions embodied in exports.Given China's important role in the global production network and division of labor,NSI may also affect industrial structure and the quality of the ecological environment to a large extent.This study analyses the network governance mechanism of China's participation in global carbon transfers.The results suggest that the technical complexity of export products and product heterogeneity do not change the positive impact of NSI on carbon emissions.

A Fast and Memory-Efficient Approach to NDN Name Lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities clearly

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.

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).

Meta-Path-Based Search and Mining in Heterogeneous Information Networks

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user＇s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.

Network Motif Detection: Algorithms, Parallel and Cloud Computing,and Related Tools

Network motif is defined as a frequent and unique subgraph pattern in a network, and the search involves counting all the possible instances or listing all patterns, testing isomorphism known as NP-hard and large amounts of repeated processes for statistical evaluation. Although many efficient algorithms have been introduced, exhaustive search methods are still infeasible and feasible approximation methods are yet implausible.Additionally, the fast and continual growth of biological networks makes the problem more challenging. As a consequence, parallel algorithms have been developed and distributed computing has been tested in the cloud computing environment as well. In this paper, we survey current algorithms for network motif detection and existing software tools. Then, we show that some methods have been utilized for parallel network motif search algorithms with static or dynamic load balancing techniques. With the advent of cloud computing services, network motif search has been implemented with MapReduce in Hadoop Distributed File System（HDFS）, and with Storm, but without statistical testing. In this paper, we survey network motif search algorithms in general, including existing parallel methods as well as cloud computing based search, and show the promising potentials for the cloud computing based motif search methods.