scholarly journals UserRBPM: User Retweet Behavior Prediction with Graph Representation Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Huihui Guo ◽  
Li Yang ◽  
Zeyu Liu
Keyword(s):  
Social Influence ◽  
Information Dissemination ◽  
Representation Learning ◽  
Social Representation ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Domain Experts ◽  
The Public ◽  
Convolutional Networks ◽  
Better Than

Social and information networks such as Facebook, Twitter, and Weibo have become the main social platforms for the public to share and exchange information, where we can easily access friends’ activities and in turn be influenced by them. Consequently, the analysis and modeling of user retweet behavior prediction have an important application value, such as information dissemination, public opinion monitoring, and product recommendation. Most of the existing solutions for user retweeting behavior prediction are usually based on network topology maps of information dissemination or designing various handcrafted rules to extract user-specific and network-specific features. However, these methods are very complex or heavily dependent on the knowledge of domain experts. Inspired by the successful use of neural networks in representation learning, we design a framework, UserRBPM, to explore potential driving factors and predictable signals in user retweet behavior. We use the graph embedding technology to extract the structural attributes of the ego network, consider the drivers of social influence from the spatial and temporal levels, and use graph convolutional networks and the graph attention mechanism to learn its potential social representation and predictive signals. Experimental results show that our proposed UserRBPM framework can significantly improve prediction performance and express social influence better than traditional feature engineering-based approaches.

scholarly journals An Attention-Based Graph Neural Network for Heterogeneous Structural Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4132-4139
Author(s):  
Huiting Hong ◽  
Hantao Guo ◽  
Yucheng Lin ◽  
Xiaoqing Yang ◽  
Zang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Heterogeneous Information ◽  
Domain Experts ◽  
Proposed Model ◽  
Meta Path ◽  
Low Dimensional ◽  
Public Datasets

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

scholarly journals MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Leon Wong ◽  
Ping Zhang ◽  
Hao-Yuan Li ◽  
...  
Keyword(s):  
High Efficiency ◽  
Drug Repositioning ◽  
Representation Learning ◽  
Computational Method ◽  
Graph Representation ◽  
Practical Applications ◽  
Study Results ◽  
Disease Associations ◽  
Better Than

Drug repositioning is an application-based solution based on mining existing drugs to find new targets, quickly discovering new drug-disease associations, and reducing the risk of drug discovery in traditional medicine and biology. Therefore, it is of great significance to design a computational model with high efficiency and accuracy. In this paper, we propose a novel computational method MGRL to predict drug-disease associations based on multi-graph representation learning. More specifically, MGRL first uses the graph convolution network to learn the graph representation of drugs and diseases from their self-attributes. Then, the graph embedding algorithm is used to represent the relationships between drugs and diseases. Finally, the two kinds of graph representation learning features were put into the random forest classifier for training. To the best of our knowledge, this is the first work to construct a multi-graph to extract the characteristics of drugs and diseases to predict drug-disease associations. The experiments show that the MGRL can achieve a higher AUC of 0.8506 based on five-fold cross-validation, which is significantly better than other existing methods. Case study results show the reliability of the proposed method, which is of great significance for practical applications.

scholarly journals Hybrid Low-Order and Higher-Order Graph Convolutional Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Fangyuan Lei ◽  
Xun Liu ◽  
Qingyun Dai ◽  
Bingo Wing-Kuen Ling ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Representation Learning ◽  
Higher Order ◽  
Semisupervised Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Proposed Model ◽  
Low Order

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.

scholarly journals Caps-OWKG: a capsule network model for open-world knowledge graph

2021 ◽  
Author(s):  
Yuhan Wang ◽  
Weidong Xiao ◽  
Zhen Tan ◽  
Xiang Zhao
Keyword(s):  
Representation Learning ◽  
Graph Representation ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Relational Structures ◽  
Open World ◽  
Latent Features ◽  
Knowledge Graphs ◽  
Low Dimensional ◽  
Better Than

AbstractKnowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.

scholarly journals Social behavior prediction with graph U-Net+

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhiyue Yan ◽  
Wenming Cao ◽  
Jianhua Ji
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Feature Extraction ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Behavior Prediction ◽  
Graph Node ◽  
Future Behavior ◽  
Decoder Architecture

AbstractWe focus on the problem of predicting social media user’s future behavior and consider it as a graph node binary classification task. Existing works use graph representation learning methods to give each node an embedding vector, then update the node representations by designing different information passing and aggregation mechanisms, like GCN or GAT methods. In this paper, we follow the fact that social media users have influence on their neighbor area, and extract subgraph structures from real-world social networks. We propose an encoder–decoder architecture based on graph U-Net, known as the graph U-Net+. In order to improve the feature extraction capability in convolutional process and eliminate the effect of over-smoothing problem, we introduce the bilinear information aggregator and NodeNorm normalization approaches into both encoding and decoding blocks. We reuse four datasets from DeepInf and extensive experimental results demonstrate that our methods achieve better performance than previous models.

scholarly journals EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

2020 ◽  
Vol 34 (04) ◽  
pp. 5363-5370 ◽  
Author(s):  
Aldo Pareja ◽  
Giacomo Domeniconi ◽  
Jie Chen ◽  
Tengfei Ma ◽  
Toyotaro Suzumura ◽  
...  
Keyword(s):  
Neural Networks ◽  
Temporal Dynamics ◽  
Representation Learning ◽  
Graph Representation ◽  
Full Time ◽  
Convolutional Network ◽  
Frequent Change ◽  
Temporal Dimension ◽  
Convolutional Networks ◽  
Node Embeddings

Graph representation learning resurges as a trending research subject owing to the widespread use of deep learning for Euclidean data, which inspire various creative designs of neural networks in the non-Euclidean domain, particularly graphs. With the success of these graph neural networks (GNN) in the static setting, we approach further practical scenarios where the graph dynamically evolves. Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics. These methods require the knowledge of a node in the full time span (including both training and testing) and are less applicable to the frequent change of the node set. In some extreme scenarios, the node sets at different time steps may completely differ. To resolve this challenge, we propose EvolveGCN, which adapts the graph convolutional network (GCN) model along the temporal dimension without resorting to node embeddings. The proposed approach captures the dynamism of the graph sequence through using an RNN to evolve the GCN parameters. Two architectures are considered for the parameter evolution. We evaluate the proposed approach on tasks including link prediction, edge classification, and node classification. The experimental results indicate a generally higher performance of EvolveGCN compared with related approaches. The code is available at https://github.com/IBM/EvolveGCN.

scholarly journals Text-Graph Enhanced Knowledge Graph Representation Learning

2021 ◽  
Vol 4 ◽  
Author(s):  
Linmei Hu ◽  
Mengmei Zhang ◽  
Shaohua Li ◽  
Jinghan Shi ◽  
Chuan Shi ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Semantic Relationships ◽  
Convolutional Networks ◽  
Gating Mechanism ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Low Dimensional ◽  
Embedding Methods

Knowledge Graphs (KGs) such as Freebase and YAGO have been widely adopted in a variety of NLP tasks. Representation learning of Knowledge Graphs (KGs) aims to map entities and relationships into a continuous low-dimensional vector space. Conventional KG embedding methods (such as TransE and ConvE) utilize only KG triplets and thus suffer from structure sparsity. Some recent works address this issue by incorporating auxiliary texts of entities, typically entity descriptions. However, these methods usually focus only on local consecutive word sequences, but seldom explicitly use global word co-occurrence information in a corpus. In this paper, we propose to model the whole auxiliary text corpus with a graph and present an end-to-end text-graph enhanced KG embedding model, named Teger. Specifically, we model the auxiliary texts with a heterogeneous entity-word graph (called text-graph), which entails both local and global semantic relationships among entities and words. We then apply graph convolutional networks to learn informative entity embeddings that aggregate high-order neighborhood information. These embeddings are further integrated with the KG triplet embeddings via a gating mechanism, thus enriching the KG representations and alleviating the inherent structure sparsity. Experiments on benchmark datasets show that our method significantly outperforms several state-of-the-art methods.

Determinants of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar, Indonesia

2019 ◽  
Vol 2 (2) ◽  
pp. 1-7
Author(s):  
Andi Samsu Rijal ◽  
Andi Mega Januarti Putri
Keyword(s):  
Language Choice ◽  
Migrant Children ◽  
Immigrant Communities ◽  
The Public ◽  
International Language ◽  
Language User ◽  
Public Area ◽  
Better Than ◽  
Language Mastery

The essence of language is human activity. Communication with language is carried out through two basic human activities; speaking and listening during the interaction in a group of people. Immigrants in Makassar city communicate with immigrant communities and Makassar people. They used English and Indonesia to communicate with others. The aims of this article were to find out determinant factors of English as language choice among Unaccompanied Migrant Children (UMC) in Makassar and why they used English as their language choice to communicate with other people out of them. The data were taken from UMC in the shelter under the auspices of Makassar’s Social Office and in the public area of Makassar. This research was a qualitative approach; it was from a sociolinguistic perspective and focuses its analysis with the language choice among UMC. This research showed that most immigrants chose English as their language choice since they were in Makassar because they have acquired better than other international language and it has been mastered naturally by doing social interaction among themselves and people outside their community. UMC had more difficulties to socialize with Indonesian than the adult of Immigrants. Other than their lack of language mastery, they also have the anxiety to adapt to other immigrants and Makassar people. English was used by UMC to show their status as a foreigner who lived in a multicultural situation. Language becomes a power for a human being and it becomes a social identity for language user in one community. During the interaction of UMC in Makassar city, the role of English as an International language is shown.

MUNDANE MIRACLES IN AWADH. ENCOUNTERS WITH THE REVEALED AND THE HIDDEN

2020 ◽  
pp. 130-140
Author(s):  
Maxim B. Demchenko ◽  
Keyword(s):  
The Other ◽  
Local People ◽  
Natural Phenomena ◽  
Mughal Empire ◽  
The Public ◽  
British Raj ◽  
The Subject ◽  
Other World ◽  
Better Than

The sphere of the unknown, supernatural and miraculous is one of the most popular subjects for everyday discussions in Ayodhya – the last of the provinces of the Mughal Empire, which entered the British Raj in 1859, and in the distant past – the space of many legendary and mythological events. Mostly they concern encounters with inhabitants of the “other world” – spirits, ghosts, jinns as well as miraculous healings following magic rituals or meetings with the so-called saints of different religions (Hindu sadhus, Sufi dervishes),with incomprehensible and frightening natural phenomena. According to the author’s observations ideas of the unknown in Avadh are codified and structured in Avadh better than in other parts of India. Local people can clearly define if they witness a bhut or a jinn and whether the disease is caused by some witchcraft or other reasons. Perhaps that is due to the presence in the holy town of a persistent tradition of katha, the public presentation of plots from the Ramayana epic in both the narrative and poetic as well as performative forms. But are the events and phenomena in question a miracle for the Avadhvasis, residents of Ayodhya and its environs, or are they so commonplace that they do not surprise or fascinate? That exactly is the subject of the essay, written on the basis of materials collected by the author in Ayodhya during the period of 2010 – 2019. The author would like to express his appreciation to Mr. Alok Sharma (Faizabad) for his advice and cooperation.

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