scholarly journals Content-Enhanced Network Embedding for Academic Collaborator Recommendation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jie Chen ◽  
Xin Wang ◽  
Shu Zhao ◽  
Yanping Zhang
Keyword(s):  
Network Structure ◽  
Real World ◽  
Representation Learning ◽  
Text Representation ◽  
Network Embedding ◽  
Academic Tasks ◽  
Semantic Relationships ◽  
Structure Text ◽  
Text Information ◽  
Real World Datasets

It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content-enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content-enhanced academic collaborator network based on the weighted text representation of each researcher. The content-enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content-enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top- k similar researchers are recommended for each target researcher. Experiment results on the real-world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.

scholarly journals Discrete Embedding for Latent Networks

2020 ◽  
Author(s):  
Hong Yang ◽  
Ling Chen ◽  
Minglong Lei ◽  
Lingfeng Niu ◽  
Chuan Zhou ◽  
...  
Keyword(s):  
Real World ◽  
Representation Learning ◽  
Mixed Integer ◽  
Information Cascades ◽  
Integer Optimization ◽  
Network Embedding ◽  
Structure Information ◽  
Proximity Matrix ◽  
Real World Datasets ◽  
Embedding Methods

Discrete network embedding emerged recently as a new direction of network representation learning. Compared with traditional network embedding models, discrete network embedding aims to compress model size and accelerate model inference by learning a set of short binary codes for network vertices. However, existing discrete network embedding methods usually assume that the network structures (e.g., edge weights) are readily available. In real-world scenarios such as social networks, sometimes it is impossible to collect explicit network structure information and it usually needs to be inferred from implicit data such as information cascades in the networks. To address this issue, we present an end-to-end discrete network embedding model for latent networks DELN that can learn binary representations from underlying information cascades. The essential idea is to infer a latent Weisfeiler-Lehman proximity matrix that captures node dependence based on information cascades and then to factorize the latent Weisfiler-Lehman matrix under the binary node representation constraint. Since the learning problem is a mixed integer optimization problem, an efficient maximal likelihood estimation based cyclic coordinate descent (MLE-CCD) algorithm is used as the solution. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art network embedding methods.

scholarly journals Learning Sequential Correlation for User Generated Textual Content Popularity Prediction

2018 ◽  
Author(s):  
Wen Wang ◽  
Wei Zhang ◽  
Jun Wang ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Real World ◽  
Information Overload ◽  
Representation Learning ◽  
Text Representation ◽  
Sequential Model ◽  
Content Popularity ◽  
Popularity Prediction ◽  
Real World Datasets ◽  
Textual Content ◽  
The Web

Popularity prediction of user generated textual content is critical for prioritizing information in the web, which alleviates heavy information overload for ordinary readers. Most previous studies model each content instance separately for prediction and thus overlook the sequential correlations between instances of a specific user. In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation. We propose a novel deep sequential model called User Memory-augmented recurrent Attention Network (UMAN). This model encodes the two correlations by updating external user memories which is further leveraged for target text representation learning and popularity prediction. The experimental results on several real-world datasets validate the benefits of considering these correlations and demonstrate UMAN achieves best performance among several strong competitors.

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

scholarly journals Network Embedding under Partial Monitoring for Evolving Networks

2019 ◽  
Author(s):  
Yu Han ◽  
Jie Tang ◽  
Qian Chen
Keyword(s):  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Actual Situation ◽  
Challenging Problem ◽  
Network Embedding ◽  
Strong Assumption ◽  
Evolving Networks ◽  
Real World Datasets ◽  
The Web

Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.

scholarly journals Multi-Aspect Embedding for Attribute-Aware Trajectories

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1149
Author(s):  
Thapana Boonchoo ◽  
Xiang Ao ◽  
Qing He
Keyword(s):  
Real World ◽  
Execution Time ◽  
State Of The Art ◽  
Representation Learning ◽  
Learning Approach ◽  
Trajectory Data ◽  
Trajectory Mining ◽  
Trajectory Similarity ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

Motivated by the proliferation of trajectory data produced by advanced GPS-enabled devices, trajectory is gaining in complexity and beginning to embroil additional attributes beyond simply the coordinates. As a consequence, this creates the potential to define the similarity between two attribute-aware trajectories. However, most existing trajectory similarity approaches focus only on location based proximities and fail to capture the semantic similarities encompassed by these additional asymmetric attributes (aspects) of trajectories. In this paper, we propose multi-aspect embedding for attribute-aware trajectories (MAEAT), a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects. MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory. Two kinds of token generation methods are proposed to extract multiple aspects from the raw trajectories, and a regularization is devised to control the importance among aspects. Extensive experiments on the benchmark and real-world datasets show the effectiveness and efficiency of the proposed MAEAT compared to the state-of-the-art and baseline methods. The results of MAEAT can well support representative downstream trajectory mining and management tasks, and the algorithm outperforms other compared methods in execution time by at least two orders of magnitude.

scholarly journals Inferring Substitutable Products with Deep Network Embedding

2019 ◽  
Author(s):  
Shijie Zhang ◽  
Hongzhi Yin ◽  
Qinyong Wang ◽  
Tong Chen ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Real World ◽  
State Of The Art ◽  
Network Embedding ◽  
Online Transactions ◽  
First Order ◽  
Substitutable Products ◽  
Deep Embedding ◽  
Real World Datasets ◽  
Explicit Feedback

On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.

scholarly journals Network Structure and Transfer Behaviors Embedding via Deep Prediction Model

2019 ◽  
Vol 33 ◽  
pp. 5041-5048 ◽  
Author(s):  
Xin Sun ◽  
Zenghui Song ◽  
Junyu Dong ◽  
Yongbo Yu ◽  
Claudia Plant ◽  
...  
Keyword(s):  
Network Structure ◽  
Real World ◽  
Short Term Memory ◽  
Experimental Studies ◽  
Local Network ◽  
Prediction Ability ◽  
Feature Representations ◽  
Biased Random Walk ◽  
Deep Embedding ◽  
Real World Datasets

Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected nodes of the huge network into feature representations is critical. As basic properties of the real-world networks, the local and global structure can be reflected by dynamical transfer behaviors from node to node. In this work, we propose a deep embedding framework to preserve the transfer possibilities among the network nodes. We first suggest a degree-weight biased random walk model to capture the transfer behaviors of the network. Then a deep embedding framework is introduced to preserve the transfer possibilities among the nodes. A network structure embedding layer is added into the conventional Long Short-Term Memory Network to utilize its sequence prediction ability. To keep the local network neighborhood, we further perform a Laplacian supervised space optimization on the embedding feature representations. Experimental studies are conducted on various real-world datasets including social networks and citation networks. The results show that the learned representations can be effectively used as features in a variety of tasks, such as clustering, visualization and classification, and achieve promising performance compared with state-of-the-art models.

scholarly journals Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

2020 ◽  
Vol 34 (01) ◽  
pp. 841-848
Author(s):  
Farzan Masrour ◽  
Tyler Wilson ◽  
Heng Yan ◽  
Pang-Ning Tan ◽  
Abdol Esfahanian
Keyword(s):  
Social Networking ◽  
Real World ◽  
Link Prediction ◽  
Representation Learning ◽  
Experimental Results ◽  
Adversarial Network ◽  
Network Link ◽  
Network Modularity ◽  
Filter Bubble ◽  
Real World Datasets

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.

Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning

2021 ◽  
pp. 016555152199061
Author(s):  
Salima Lamsiyah ◽  
Abdelkader El Mahdaouy ◽  
Saïd El Alaoui Ouatik ◽  
Bernard Espinasse
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Representation Learning ◽  
Fine Tuning ◽  
Text Representation ◽  
Document Summarization ◽  
Semantic Relationships ◽  
Benchmark Datasets ◽  
Tuning Methods ◽  
Fine Tune

Text representation is a fundamental cornerstone that impacts the effectiveness of several text summarization methods. Transfer learning using pre-trained word embedding models has shown promising results. However, most of these representations do not consider the order and the semantic relationships between words in a sentence, and thus they do not carry the meaning of a full sentence. To overcome this issue, the current study proposes an unsupervised method for extractive multi-document summarization based on transfer learning from BERT sentence embedding model. Moreover, to improve sentence representation learning, we fine-tune BERT model on supervised intermediate tasks from GLUE benchmark datasets using single-task and multi-task fine-tuning methods. Experiments are performed on the standard DUC’2002–2004 datasets. The obtained results show that our method has significantly outperformed several baseline methods and achieves a comparable and sometimes better performance than the recent state-of-the-art deep learning–based methods. Furthermore, the results show that fine-tuning BERT using multi-task learning has considerably improved the performance.

scholarly journals Trajectory Similarity Learning with Auxiliary Supervision and Optimal Matching

2020 ◽  
Author(s):  
Hanyuan Zhang ◽  
Xinyu Zhang ◽  
Qize Jiang ◽  
Baihua Zheng ◽  
Zhenbang Sun ◽  
...  
Keyword(s):  
Real World ◽  
Representation Learning ◽  
Similarity Learning ◽  
Trajectory Data ◽  
Optimal Matching ◽  
Training Samples ◽  
Trajectory Similarity ◽  
Similarity Computation ◽  
Real World Datasets ◽  
Relationship Of

Trajectory similarity computation is a core problem in the field of trajectory data queries. However, the high time complexity of calculating the trajectory similarity has always been a bottleneck in real-world applications. Learning-based methods can map trajectories into a uniform embedding space to calculate the similarity of two trajectories with embeddings in constant time. In this paper, we propose a novel trajectory representation learning framework Traj2SimVec that performs scalable and robust trajectory similarity computation. We use a simple and fast trajectory simplification and indexing approach to obtain triplet training samples efficiently. We make the framework more robust via taking full use of the sub-trajectory similarity information as auxiliary supervision. Furthermore, the framework supports the point matching query by modeling the optimal matching relationship of trajectory points under different distance metrics. The comprehensive experiments on real-world datasets demonstrate that our model substantially outperforms all existing approaches.

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