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1.
《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
2.
《Information processing & management》2022,59(6):103076
Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR). 相似文献
3.
在资源有限条件下,网络联结的构建,能使企业从联结关系中获取资源,从而促进战略绩效提升,加强企业的竞争能力。但已有研究未能从相关理论角度,挖掘网络联结、资源获取与提升企业战略绩效路径以及内在机理。以资源基础观与组织学习理论视角,采用案例研究法,以长城汽车公司为研究对象,讨论“不同网络联结阶段的结构特征、同资源获取方式与组织学习形式之间的互动关系,提升企业战略绩效的路径与机理”。通过企业网络联结成长阶段和网络联结成熟阶段的对比分析,构建出企业不同网络联结阶段的“网络联结、资源获取与组织学习”的互动关系对企业战略绩效作用的理论框架。研究结论揭示了网络联结影响企业战略绩效的深层次原因,凸显了资源获取和组织学习与之共演化的中介作用,进而深入了解企业网络联结的异变机理与作用发挥效应,为企业整合网络联结、获取资源优势、加强组织学习、提高企业战略绩效提供指导。 相似文献
4.
《Information processing & management》2022,59(4):102956
Several approaches focus on how to automatically capture the latent features from original diffusion data and predict the future scale of cascades utilizing a black box framework. However, they ignore the penetrating insight into the underlying mechanism that how each participant is involved in the cascade. In this work, we bridge the gap between prediction and understanding of information diffusion by incorporating deep learning techniques and social psychology. To characterize individual participation driven by both subjective and objective impetus and integrate it into the macro-level cascade, we propose an end-to-end model, named PFDID, which is designed based on the field dynamics theory of psychology, including the intrinsic cognition field and the extrinsic environment field. We represent these two field dynamics respectively with the pairwise semantic relation between the message itself and corresponding comment and the forwarder’s micro-community activity embedding to provide educated explanations for forwarding behaviour. Afterwards, the cross infusion mechanism is designed to calculate the mutual influence of inhomogeneous field dynamics inside users and cross influence of homogeneous field dynamics among individuals, whose output is fed into the diffusion network aggregation layer for the cascade size prediction. Extensive experiments on two typical social networks, Sina Weibo and Twitter, manifest that the proposed PFDID outperforms state-of-the-art approaches. Our model achieves excellent prediction results, with MSLE = 1.856 on Sina Weibo and MSLE = 1.962 on Twitter, providing 6.54% and 10.53% relative performance gains, respectively. Furthermore, the interpretability is also discussed based on detailed visualization. We observe that the psychological impetus behind social behaviour varies mainly following two patterns with the spread of information, including gradual change and joint influence. Additionally, the indirect dependencies have also been verified. 相似文献
5.
《Information processing & management》2022,59(3):102916
Graph convolutional network (GCN) is a powerful tool to process the graph data and has achieved satisfactory performance in the task of node classification. In general, GCN uses a fixed graph to guide the graph convolutional operation. However, the fixed graph from the original feature space may contain noises or outliers, which may degrade the effectiveness of GCN. To address this issue, in this paper, we propose a robust graph learning convolutional network (RGLCN). Specifically, we design a robust graph learning model based on the sparse constraint and strong connectivity constraint to achieve the smoothness of the graph learning. In addition, we introduce graph learning model into GCN to explore the representative information, aiming to learning a high-quality graph for the downstream task. Experiments on citation network datasets show that the proposed RGLCN outperforms the existing comparison methods with respect to the task of node classification. 相似文献
6.
创业活动的成败与创业者对其社会网络的利用有直接关系。在482个再创业调研样本的基础之上,从个人和团队社会网络两个方面构建创业社会网络对再创业绩效作用的理论模型,分析不同创业类型、创业领域下的作用路径机制。研究结果表明:创业社会网络对再创业绩效的作用机制路径有三条,其中资源整合、创业学习和创业动态能力分别起到中介作用。在不同的创业类型和不同的创业领域下,个人社会网络和团队社会网络对再创业绩效的作用程度不同,并且创业社会网络对企业再创业绩效的作用路径、作用特征和作用效果也存在显著的差异。通过上述分析,不仅可以丰富创业者社会网络行为的理论知识,而且为提升再创业绩效提供现实指导意义。 相似文献
7.
《Information processing & management》2016,52(5):911-922
In social networks, identifying influential nodes is essential to control the social networks. Identifying influential nodes has been among one of the most intensively studies of analyzing the structure of networks. There are a multitude of evaluation indicators of node importance in social networks, such as degree, betweenness and cumulative nomination and so on. But most of the indicators only reveal one characteristic of the node. In fact, in social networks, node importance is not affected by a single factor, but is affected by a number of factors. Therefore, the paper puts forward a relatively comprehensive and effective method of evaluation node importance in social networks by using the multi-objective decision method. Firstly, we select several different representative indicators given a certain weight. We regard each node as a solution and different indicators of each node as the solution properties. Then through calculating the closeness degree of each node to the ideal solution, we obtain evaluation indicator of node importance in social networks. Finally, we verify the effectiveness of the proposed method experimentally on a few actual social networks. 相似文献
8.
Studying information flow between node clusters can be conceptualised as an important challenge for the knowledge management research and practice community. We are confronted with issues related to establishing links between nodes and/or clusters during the process of information flow and search transfer in large distributed networks. In the case of missing socio-technical links, social networks can be instrumental in supporting the communities of practice interested in sharing and transferring knowledge across informal links. A comprehensive review of methodology for detecting missing links is provided. The proportion of common neighbours was selected as best practice to elicit missing links from a large health insurance data set. Weights were based on geographical arrangements of providers and the dollar value of transactions. The core network was elicited based on statistical thresholds. Suspicious, possibly fraudulent, behaviour is highlighted based on social network measures of the core. Our findings are supported by a health insurance industry expert panel. 相似文献
9.
借鉴社会网络理论中的濡染模型,将网络节点行为变量引入知识生产函数,对模型进行扩展,基于扩展后的模型考察我国电子信息领域和生物医药领域区域间技术交易网络、吸收能力与区域创新产出的关系。研究表明：（1）区域间知识溢出模式具有明显的技术领域特征,在电子信息领域,不同区域节点是通过模仿结构相似的竞争者的行为获取外部知识;在生物医药领域,不同区域节点是在互惠网络中获取新知识。（2）知识吸收能力对区域间技术交易网络与区域创新产出的关系具有调节作用。吸收能力高的区域和吸收能力低的区域,都可以通过嵌入区域间技术交易网络,实现区域创新能力提升。 相似文献
10.
不同网络嵌入方式对企业创新绩效的影响是存在显著差异的。构建了网络嵌入、学习能力和技术创新绩效之间的理论模型,通过运用结构方程模型对广东省高新技术与民营科技型企业为样本的问卷调查数据进行实证分析。研究结果表明:(1)网络结构嵌入对技术创新绩效没有直接的显著影响,也无法通过学习能力的中介对其产生间接的影响作用;(2)网络关系嵌入对技术创新绩效不仅有直接显著的正向影响,而且还能通过学习能力的部分中介作用对技术创新绩效起到显著的正向影响;(3)在小规模企业中,网络密度对于技术创新绩效的作用并不显著。研究结论进一步深化了技术创新理论,对企业技术创新的提升有一定指导意义。 相似文献
11.
《Information processing & management》2023,60(3):103301
Hashing has been an emerging topic and has recently attracted widespread attention in multi-modal similarity search applications. However, most existing approaches rely on relaxation schemes to generate binary codes, leading to large quantization errors. In addition, amounts of existing approaches embed labels into the pairwise similarity matrix, leading to expensive time and space costs and losing category information. To address these issues, we propose an Efficient Discrete Matrix factorization Hashing (EDMH). Specifically, EDMH first learns the latent subspaces for individual modality through matrix factorization strategy, which preserves the semantic structure representation information of each modality. In particular, we develop a semantic label offset embedding learning strategy, improving the stability of label embedding regression. Furthermore, we design an efficient discrete optimization scheme to generate compact binary codes discretely. Eventually, we present two efficient learning strategies EDMH-L and EDMH-S to pursue high-quality hash functions. Extensive experiments on various widely-used databases verify that the proposed algorithms produce significant performance and outperform some state-of-the-art approaches, with an average improvement of 2.50% (for Wiki), 2.66% (for MIRFlickr) and 2.25% (for NUS-WIDE) over the best available results, respectively. 相似文献
12.
《Information processing & management》2022,59(5):103009
The rapid development of online social media makes Abusive Language Detection (ALD) a hot topic in the field of affective computing. However, most methods for ALD in social networks do not take into account the interactive relationships among user posts, which simply regard ALD as a task of text context representation learning. To solve this problem, we propose a pipeline approach that considers both the context of a post and the characteristics of interaction network in which it is posted. Specifically, our method is divided into pre-training and downstream tasks. First, to capture fine contextual features of the posts, we use Bidirectional Encoder Representation from Transformers (BERT) as Encoder to generate sentence representations. Later, we build a Relation-Special Network according to the semantic similarity between posts as well as the interaction network structural information. On this basis, we design a Relation-Special Graph Neural Network (RSGNN) to spread effective information in the interaction network and learn the classification of texts. The experiment proves that our method can effectively improve the detection effect of abusive posts over three public datasets. The results demonstrate that injecting interaction network structure into the abusive language detection task can significantly improve the detection results. 相似文献
13.
《Information processing & management》2020,57(6):102353
In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional social influence computation method in EBSNs. Therefore, to quantify the pairwise social influence accurately in EBSNs, we first propose to compute two types of social influences, i.e., structure-based social influence and behaviour-based social influence, by utilizing the online social network structure and offline social behaviours of users. In particular, based on the specific features of EBSNs, the similarities of user preference on three aspects (i.e., topics, regions and organizers) are utilized to measure the behaviour-based social influence. Then, we obtain the unified pairwise social influence by combining these two types of social influences through a weight function. Next, we present a social influence based community detection algorithm which is referred to as SICD. In SICD, inspired by the nonlinear feature learning ability of the autoencoder, we first devise a neighborhood based deep autoencoder algorithm to obtain nonlinear community-oriented latent representations of users, and then utilize the k-means algorithm for community detection. Experimental results conducted on real-world dataset show the effectiveness of our proposed algorithm. 相似文献
14.
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
15.
《Information processing & management》2023,60(2):103197
When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. 相似文献
16.
《Information processing & management》2023,60(2):103221
Disease spread control is a challenging task with growing importance in recent years. Infectious disease networks have been proven to be a helpful resource for controlling the epidemic by targeting a smaller population. However, the information on these networks is often imprecise, diffused, concealed, and misleading, making it challenging to obtain a complete set of real-world data, i.e., some links might be missing, which can be a risk to the widespread of the pandemic. The former studies on infectious disease networks ignore the influence of neighborhood missing links in the infectious disease network topology, thus massively targeting the irrelevant population, resulting in poor epidemic control performance. In this paper, to address such a problem, we study how a small portion of the population should be targeted with incomplete network information to effectively prevent the pandemic. We propose an algorithm, namely, the Neighborhood Relation Aware Network Dismantling Algorithm (NRAND), to efficiently address the infectious disease network’s dismantling problem. For comparison, four network dismantling strategies are employed in our experiments. An extensive empirical study of real-world networks suggests that the proposed algorithm NRAND’s dismantling performance is significantly greater than the state-of-the-art algorithms, indicating that NRAND can be a smarter option for dismantling real-world infectious disease networks. 相似文献
17.
Mining direct antagonistic communities in signed social networks 总被引：1，自引：1，他引：0
David Lo Didi Surian Philips Kokoh Prasetyo Kuan Zhang Ee-Peng Lim 《Information processing & management》2013
Social networks provide a wealth of data to study relationship dynamics among people. Most social networks such as Epinions and Facebook allow users to declare trusts or friendships with other users. Some of them also allow users to declare distrusts or negative relationships. When both positive and negative links co-exist in a network, some interesting community structures can be studied. In this work, we mine Direct Antagonistic Communities (DACs) within such signed networks. Each DAC consists of two sub-communities with positive relationships among members of each sub-community, and negative relationships among members of the other sub-community. Identifying direct antagonistic communities is an important step to understand the nature of the formation, dissolution, and evolution of such communities. Knowledge about antagonistic communities allows us to better understand and explain behaviors of users in the communities. 相似文献
18.
《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
19.
《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
20.
[目的/意义] 在大规模社交网络中快速搜索关键节点对于舆情的引导和控制具有重要意义。[方法/过程] 本文提出一种适用于社交网络的局部中心性关键节点识别算法,该方法综合评估了节点的K壳、自身的聚集特性以及邻居的扩散特性和节点自身传播状态,同时体现了节点在空间上的网络位置和邻居的拓扑结构以及在时间上演化特征,评价指标更加全面高效。[结果/结论] 实验结果表明,该方法识别的关键节点对网络鲁棒性的影响与介数中心性接近,但计算仅基于节点局部信息,时间复杂度低。剔除这些节点后网络的连通性受到较大影响,网络聚类系数降低,平均路径长度增加。同时,利用SIR传播模型模拟验证,以该算法识别的关键节点为初始传播源可提升信息传播范围和平均传播速度。 相似文献