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1.
基于模体的科学家合作网络基元特征分析 总被引：2，自引：0，他引：2
基于复杂网络构建科学家合作网络并分析其全局结构特征,是理解科学家间合作机制的重要手段和研究热点。实际上,辨识其局部结构可以进一步增进对合作机制的理解。基于复杂网络局部结构研究的前沿理念和方法——模体,构建并辨析若干大型科学家合作网络的基元特征:模体特性、子图浓度以及子图自下而上的组合机制。研究表明不同领域科学家合作网络的模体与反模体形式具有共性特征,网络的子图相对浓度则因不同领域科学家之间合作强度及网络发展程度差异而具有不同的分布特征。此外,科学家合作网络具有相似的自下而上构建机制。研究提供了基于模体的科学家合作网络结构辨识方法。 相似文献
2.
《Information processing & management》2023,60(3):103292
Dynamic link prediction is a critical task in network research that seeks to predict future network links based on the relative behavior of prior network changes. However, most existing methods overlook mutual interactions between neighbors and long-distance interactions and lack the interpretability of the model’s predictions. To tackle the above issues, in this paper, we propose a temporal group-aware graph diffusion network(TGGDN). First, we construct a group affinity matrix to describe mutual interactions between neighbors, i.e., group interactions. Then, we merge the group affinity matrix into the graph diffusion to form a group-aware graph diffusion, which simultaneously captures group interactions and long-distance interactions in dynamic networks. Additionally, we present a transformer block that models the temporal information of dynamic networks using self-attention, allowing the TGGDN to pay greater attention to task-related snapshots while also providing interpretability to better understand the network evolutionary patterns. We compare the proposed TGGDN with state-of-the-art methods on five different sizes of real-world datasets ranging from 1k to 20k nodes. Experimental results show that TGGDN achieves an average improvement of 8.3% and 3.8% in terms of ACC and AUC on all datasets, respectively, demonstrating the superiority of TGGDN in the dynamic link prediction task. 相似文献
3.
《Information processing & management》2023,60(5):103418
Link prediction, which aims to predict future or missing links among nodes, is a crucial research problem in social network analysis. A unique few-shot challenge is link prediction on newly emerged link types without sufficient verification information in heterogeneous social networks, such as commodity recommendation on new categories. Most of current approaches for link prediction rely heavily on sufficient verified link samples, and almost ignore the shared knowledge between different link types. Hence, they tend to suffer from data scarcity in heterogeneous social networks and fail to handle newly emerged link types where has no sufficient verified link samples. To overcome this challenge, we propose a model based on meta-learning, called the meta-learning adaptation network (MLAN), which acquires transferable knowledge from historical link types to improve the prediction performance on newly emerged link types. MLAN consists of three main components: a subtask slicer, a meta migrator, and an adaptive predictor. The subtask slicer is responsible for generating community subtasks for the link prediction on historical link types. Subsequently, the meta migrator simultaneously completes multiple community subtasks from different link types to acquire transferable subtask-shared knowledge. Finally, the adaptive predictor employs the parameters of the meta migrator to fuse the subtask-shared knowledge from different community subtasks and learn the task-specific knowledge of newly emerged link types. Experimental results conducted on real-world social media datasets prove that our proposed MLAN outperforms state-of-the-art models in few-shot link prediction in heterogeneous social networks. 相似文献
4.
《Information processing & management》2022,59(2):102869
User-created automation applets to connect IoT devices and applications have become popular and widely available. Exploring those applets enables us to grasp the patterns of how users are utilizing and maximizing the power of connection by themselves, which can deliver practical implications for IoT service design. This study builds an IoT application network with the data of the IFTTT(if this then that) platform which is the most popular platform for self-automation of IoT services. The trigger-action relationships of the IFTTT applets currently activated are collected and used to construct an IoT application network whose nodes are IoT service channels, and links represent their connections. The constructed IoT network is then embedded by the node2vec technique, an algorithmic framework for representational learning of nodes in networks. Clustering the embedded nodes produces the four clusters of IoT usage patterns: Smart Home, Activity Tracking, Information Digest, and Lifelogging & Sharing. We also predict the IoT application network using node2vec-based link prediction with several machine learning classifiers to identify promising connections between IoT applications. Feasible service scenarios are then generated from predicted links between IoT applications. The findings and the proposed approach can offer IoT service providers practical implications for enhancing user experiences and developing new services. 相似文献
5.
Depression is one of the most common mental health problems worldwide. The diagnosis of depression is usually done by clinicians based on mental status questionnaires and patient's self-reporting. Not only do these methods highly depend on the current mood of the patient, but also people who experience mental illness are often reluctantly seeking help. Social networks have become a popular platform for people to express their feelings and thoughts with friends and family. With the substantial amount of data in social networks, there is an opportunity to try designing novel frameworks to identify those at risk of depression. Moreover, such frameworks can provide clinicians and hospitals with deeper insights about depressive behavioral patterns, thereby improving diagnostic process. In this paper, we propose a big data analytics framework to detect depression for users of social networks. In addition to syntactic and syntax features, it focuses on pragmatic features toward modeling the intention of users. User intention represents the true motivation behind social network behaviors. Moreover, since the behaviors of user's friends in the network are believed to have an influence on the user, the framework also models the influence of friends on the user's mental states. We evaluate the performance of the proposed framework on a massive real dataset obtained from Facebook and show that the framework outperforms existing methods for diagnosing user-level depression in social networks. 相似文献
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Transients are fundamental to ecological systems with significant implications to management, conservation and biological control. We uncover a type of transient synchronization behavior in spatial ecological networks whose local dynamics are of the chaotic, predator–prey type. In the parameter regime where there is phase synchronization among all the patches, complete synchronization (i.e. synchronization in both phase and amplitude) can arise in certain pairs of patches as determined by the network symmetry—henceforth the phenomenon of ‘synchronization within synchronization.’ Distinct patterns of complete synchronization coexist but, due to intrinsic instability or noise, each pattern is a transient and there is random, intermittent switching among the patterns in the course of time evolution. The probability distribution of the transient time is found to follow an algebraic scaling law with a divergent average transient lifetime. Based on symmetry considerations, we develop a stability analysis to understand these phenomena. The general principle of symmetry can also be exploited to explain previously discovered, counterintuitive synchronization behaviors in ecological networks. 相似文献
7.
在知识经济时代,学科知识的传播与扩散促进了学科的协同、交叉、融合、发展与创新。文章利用复杂网络算法对学科引证知识扩散时序演化网络进行动态链路预测分析,以期探索学科知识流动结构变化及演进态势,为学科知识管理及决策制定提供可资借鉴的理论和实践参考。文章以学科引证知识扩散时序演化网络结构信息为基础,采用10项基于局部信息的相似性指标分别对无权和加权知识扩散网络进行动态链路预测分析,并将各指标的预测性能进行了对比。最后,利用无权RA指标和加权AA指标对学科引证知识扩散态势进行了预测。研究表明:不同指标的预测精度在不同的时间段内会动态变化;在学科引证知识扩散网络中,存在一定程度的弱连接效应;不同链路预测指标在无权和加权学科引证知识扩散网络中的适用性存在一定差异。 相似文献
8.
In network science, the non-homogeneity of node degrees has been a concerning issue for study. Yet, with today''s modern web technologies, the traditional social communication topologies have evolved from node-central structures into online cycle-based communities, urgently requiring new network theories and tools. Switching the focus from node degrees to network cycles could reveal many interesting properties from the perspective of totally homogenous networks or sub-networks in a complex network, especially basic simplexes (cliques) such as links and triangles. Clearly, compared with node degrees, it is much more challenging to deal with network cycles. For studying the latter, a new clique vector-space framework is introduced in this paper, where the vector space with a basis consisting of links has a dimension equal to the number of links, that with a basis consisting of triangles has the dimension equal to the number of triangles and so on. These two vector spaces are related through a boundary operator, for example mapping the boundary of a triangle in one space to the sum of three links in the other space. Under the new framework, some important concepts and methodologies from algebraic topology, such as characteristic number, homology group and Betti number, will play a part in network science leading to foreseeable new research directions. As immediate applications, the paper illustrates some important characteristics affecting the collective behaviors of complex networks, some new cycle-dependent importance indexes of nodes and implications for network synchronization and brain-network analysis. 相似文献
9.
《Information processing & management》2022,59(3):102917
Nowadays, signed network has become an important research topic because it can reflect more complex relationships in reality than traditional network, especially in social networks. However, most signed network methods that achieve excellent performance through structure information learning always neglect neutral links, which have unique information in social networks. At the same time, previous approach for neutral links cannot utilize the graph structure information, which has been proved to be useful in node embedding field. Thus, in this paper, we proposed the Signed Graph Convolutional Network with Neutral Links (NL-SGCN) to address the structure information learning problem of neutral links in signed network, which shed new insight on signed network embedding. In NL-SGCN, we learn two representations for each node in each layer from both inner character and outward attitude aspects and propagate their information by balance theory. Among these three types of links, information of neutral links will be limited propagated by the learned coefficient matrix. To verify the performance of the proposed model, we choose several classical datasets in this field to perform empirical experiment. The experimental result shows that NL-SGCN significantly outperforms the existing state-of-the-art baseline methods for link prediction in signed network with neutral links, which supports the efficacy of structure information learning in neutral links. 相似文献
10.
《Information processing & management》2020,57(2):102056
Identifying and extracting user communities is an important step towards understanding social network dynamics from a macro perspective. For this reason, the work in this paper explores various aspects related to the identification of user communities. To date, user community detection methods employ either explicit links between users (link analysis), or users’ topics of interest in posted content (content analysis), or in tandem. Little work has considered temporal evolution when identifying user communities in a way to group together those users who share not only similar topical interests but also similar temporal behavior towards their topics of interest. In this paper, we identify user communities through multimodal feature learning (embeddings). Our core contributions can be enumerated as (a) we propose a new method for learning neural embeddings for users based on their temporal content similarity; (b) we learn user embeddings based on their social network connections (links) through neural graph embeddings; (c) we systematically interpolate temporal content-based embeddings and social link-based embeddings to capture both social network connections and temporal content evolution for representing users, and (d) we systematically evaluate the quality of each embedding type in isolation and also when interpolated together and demonstrate their performance on a Twitter dataset under two different application scenarios, namely news recommendation and user prediction. We find that (1) content-based methods produce higher quality communities compared to link-based methods; (2) methods that consider temporal evolution of content, our proposed method in particular, show better performance compared to their non-temporal counter-parts; (3) communities that are produced when time is explicitly incorporated in user vector representations have higher quality than the ones produced when time is incorporated into a generative process, and finally (4) while link-based methods are weaker than content-based methods, their interpolation with content-based methods leads to improved quality of the identified communities. 相似文献
11.
Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks. 相似文献
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13.
A visual display of the most important universities in the world is the aim of this paper. It shows the topological characteristics and describes the web relationships among universities of different countries and continents. The first 1000 higher education institutions from the Ranking Web of World Universities were selected and their link relationships were obtained from Yahoo! Search. Network graphs and geographical maps were built from the search engine data. Social network analysis techniques were used to analyse and describe the structural properties of the whole of the network and its nodes. The results show that the world-class university network is constituted from national sub-networks that merge in a central core where the principal universities of each country pull their networks toward international link relationships. The United States dominates the world network, and within Europe the British and the German sub-networks stand out. 相似文献
14.
This study considers merging dynamical networks (relative sensing networks in this paper) in terms of a stability margin criterion. The main motivation of this consideration is that merging can cause a significant drop in the stability margin of merged network with respect to the original networks initially with ample stability margins. In this paper, various types of network merging (i.e. undirected/directed homogeneous/heterogeneous dynamical network merging via one-way/two-way links) are analysed to show their effects on the stability margin. In particular, it is shown that (1) merging with one-way links yields the stability margin less than the original networks’; (2) merging undirected homogeneous networks with two-way links results in a stability margin being at least a quantity solely characterized by the positive realness (PRness) of SISO (Single-Input-Single-Output) local dynamics; (3) the quantity depends both on the PRness of SISO local dynamics and the eigenvalues of Laplacian matrix, in case of merging directed homogeneous networks with two-way links; (4) two-way merging using multiple nodes may allow for a large increase in the stability margin; and (5) merging heterogeneous networks may be simply treated as merging homogeneous networks by exploiting the design of link dynamics. Several numerical results are presented to show their consistency with the performed analysis. 相似文献
15.
Yuxin Chen Yang Shen Pei Lin Ding Tong Yixin Zhao Stefano Allesina Xu Shen Chung-I Wu 《国家科学评论(英文版)》2019,6(6):1176
Food web and gene regulatory networks (GRNs) are large biological networks, both of which can be analyzed using the May–Wigner theory. According to the theory, networks as large as mammalian GRNs would require dedicated gene products for stabilization. We propose that microRNAs (miRNAs) are those products. More than 30% of genes are repressed by miRNAs, but most repressions are too weak to have a phenotypic consequence. The theory shows that (i) weak repressions cumulatively enhance the stability of GRNs, and (ii) broad and weak repressions confer greater stability than a few strong ones. Hence, the diffuse actions of miRNAs in mammalian cells appear to function mainly in stabilizing GRNs. The postulated link between mRNA repression and GRN stability can be seen in a different light in yeast, which do not have miRNAs. Yeast cells rely on non-specific RNA nucleases to strongly degrade mRNAs for GRN stability. The strategy is suited to GRNs of small and rapidly dividing yeast cells, but not the larger mammalian cells. In conclusion, the May–Wigner theory, supplanting the analysis of small motifs, provides a mathematical solution to GRN stability, thus linking miRNAs explicitly to ‘developmental canalization’. 相似文献
16.
从供应商网络视角,研究供应商创新性、网络能力对制造企业产品创新绩效的影响,并重点考察供应商网络结构对上述关系的调节作用。对285个有效样本的实证研究显示,供应商创新性和网络能力对产品创新绩效有正向影响。此外,制造企业中心性和网络关系强度正向调节供应商创新性与产品创新绩效间的关系;网络关系密度负向调节供应商创新性与产品创新绩效间的关系;制造企业中心性、网络关系强度和网络内部差异性正向调节网络能力与产品创新绩效间的关系;网络关系密度负向调节网络能力与产品创新绩效间的关系。研究表明,供应商创新性和网络能力对制造企业产品创新都有积极作用,而恰当的供应商网络结构将促进这一作用。 相似文献
17.
Connection and stratification in research collaboration: An analysis of the COLLNET network 总被引：1，自引：0，他引：1
Co-authorship among scientists represents a prototype of a social network. By mapping the graph containing all relevant publications of members in an international collaboration network: COLLNET, we infer the structural mechanisms that govern the topology of this social system. The structure of the network affects the information available to individuals, and their opportunities to collaborate. The structure of the network also affects the overall flow of information, and the nature of the scientific community. We present a number of measures of both the macro- (whole-network) and micro- (actor-centered) structure of collaboration, and apply these to COLLNET. We find that this scientific community displays many aspects of a “small-world,” and is somewhat vulnerable to disruption should major figures become inactive. We also find inequality in the roles played by individuals in the network. The inequalities, however, do not create a closed and isolated “core” or elite. 相似文献
18.
Szabolcs Feczak Liaquat Hossain 《The Journal of High Technology Management Research》2011,22(2):121-140
The performance of work groups and in particular geographically distributed ones is negatively affected by communication issues and task dependencies.Contemporary science suggests social link optimization apart from improving the technical aspects to address these issues. In our study, we focus on distributed coordination and project performance. Social network structure and coordination performance variables are described by our framework with regards to distributed coordination during bug fixing process. Based on the model and the literature reviewed, we propose two propositions—(i) the level of interconnectedness has a negative relation with coordination performance; and (ii) centrality social network measures have positive relation with coordination performance variables. We use a sample of 415 Open Source Projects hosted on SourceForge.net. The results suggest that both propositions are correct. Furthermore, in the methods section implementation of an automated process is introduced to build graph definitions in adjacency matrix or NCOL format from thousands of forum threads. We describe the implementation of a novel method to plot sociograms in batch from hundreds of graph definitions automatically and calculate network centrality and density measures for all of them at the same time. Finally, we suggest the implications of this study to software development project management research. 相似文献
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