An Introduction To Management Science Quantitative Approaches To Decision Making

Identifying influential spreaders in complex networks is a crucial problem which relates to wide applications. Many methods based on the global information such as $k$-shell and PageRank have been applied to rank spreaders. However, most of related previous works overwhelmingly focus on the number of paths for propagation, while whether the paths are diverse enough is usually overlooked. Generally, the spreading ability of a node might not be strong if its propagation depends on one or two paths while the other paths are dead ends. In this Letter, we introduced the concept of path diversity and find that it can largely improve the ranking accuracy. We further propose a local method combining the information of path number and path diversity to identify influential nodes in complex networks. This method is shown to outperform many well-known methods in both undirected and directed networks. Moreover, the efficiency of our method makes it possible to be applied to very large systems.

Path diversity improves the identification of influential spreaders

Detecting hot social events (e.g., political scandal, momentous meetings, natural hazards, etc.) from social messages is crucial as it highlights significant happenings to help people understand the real world. On account of the streaming nature of social messages, incremental social event detection models in acquiring, preserving, and updating messages over time have attracted great attention. However, the challenge is that the existing event detection methods towards streaming social messages are generally confronted with ambiguous events features, dispersive text contents, and multiple languages, and hence result in low accuracy and generalization ability. In this paper, we present a novel reinForced, incremental and cross-lingual social Event detection architecture, namely FinEvent, from streaming social messages. Concretely, we first model social messages into heterogeneous graphs integrating both rich meta-semantics and diverse meta-relations, and convert them to weighted multi-relational message graphs. Second, we propose a new reinforced weighted multi-relational graph neural network framework by using a Multi-agent Reinforcement Learning algorithm to select optimal aggregation thresholds across different relations/edges to learn social message embeddings. To solve the long-tail problem in social event detection, a balanced sampling strategy guided Contrastive Learning mechanism is designed for incremental social message representation learning. Third, a new Deep Reinforcement Learning guided density-based spatial clustering model is designed to select the optimal minimum number of samples required to form a cluster and optimal minimum distance between two clusters in social event detection tasks. Finally, we implement incremental social message representation learning based on knowledge preservation on the graph neural network and achieve the transferring cross-lingual social event detection. We conduct extensive experiments to evaluate the FinEvent on Twitter streams, demonstrating a significant and consistent improvement in model quality with 14%–118%, 8%–170%, and 2%–21% increases in performance on offline, online, and cross-lingual social event detection tasks.

Reinforced, Incremental and Cross-Lingual Event Detection From Social Messages

Critical nodes of complex networks play a crucial role in effective information spreading. There are many methods have been proposed to identify critical nodes in complex networks, ranging from centralities of nodes to diffusion-based processes. Most of them try to find what kind of structure will make the node more influential. In this paper, inspired by the concept of graph convolutional networks(GCNs), we convert the critical node identification problem in complex networks into a regression problem. Considering adjacency matrices of networks and convolutional neural networks(CNNs), a simply yet effectively method named R C N N is presented to identify critical nodes with the best spreading ability. In this approach, we can generate feature matrix for each node and use a convolutional neural network to train and predict the influence of nodes. Experimental results on nine synthetic and fifteen real networks show that under Susceptible–Infected–Recovered (SIR) model, R C N N outperforms the traditional benchmark methods on identifying critical nodes under spreading dynamic.

Identifying critical nodes in complex networks via graph convolutional networks

Recommender systems have become prosperous nowadays, designed to predict users' potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks~(GNNs) also provide recommender systems with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. To this end, we devise a novel framework \textbf{Fe}drated \textbf{So}cial recommendation with \textbf{G}raph neural network (FeSoG). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

Federated Social Recommendation with Graph Neural Network

Identifying a set of influential nodes is an important topic in complex networks which plays a crucial role in many applications, such as market advertising, rumor controlling, and predicting valuable scientific publications. In regard to this, researchers have developed algorithms from simple degree methods to all kinds of sophisticated approaches. However, a more robust and practical algorithm is required for the task. In this paper, we propose the EnRenew algorithm aimed to identify a set of influential nodes via information entropy. Firstly, the information entropy of each node is calculated as initial spreading ability. Then, select the node with the largest information entropy and renovate its l-length reachable nodes' spreading ability by an attenuation factor, repeat this process until specific number of influential nodes are selected. Compared with the best state-of-the-art benchmark methods, the performance of proposed algorithm improved by 21.1%, 7.0%, 30.0%, 5.0%, 2.5%, and 9.0% in final affected scale on CEnew, Email, Hamster, Router, Condmat, and Amazon network, respectively, under the Susceptible-Infected-Recovered (SIR) simulation model. The proposed algorithm measures the importance of nodes based on information entropy and selects a group of important nodes through dynamic update strategy. The impressive results on the SIR simulation model shed light on new method of node mining in complex networks for information spreading and epidemic prevention.

Influential Nodes Identification in Complex Networks via Information Entropy

Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social recommendation frameworks to aggregate both social and user-item interaction information simultaneously. However, most existing methods neglect the social inconsistency problem, which intuitively suggests that social links are not necessarily consistent with the rating prediction process. Social inconsistency can be observed from both context-level and relation-level. Therefore, we intend to empower the GNN model with the ability to tackle the social inconsistency problem. We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors. Besides, we employ the relation attention mechanism to assign consistent relations with high importance factors for aggregation. Experiments on two real-world datasets verify the model effectiveness.

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation



Lectins are a diverse group of glycoproteins or glycoconjugate proteins
that can be extracted from plants, invertebrates and higher animals. Cancerlectins, a kind of lectins,
which play a key role in the process of tumor cells interacting with each other and are being employed
as therapeutic agents. A full understanding of cancerlectins is significant because it provides
a tool for the future direction of cancer therapy.



To develop an accurate and practically useful timesaving tool to identify cancerlectins.
A novel sequence-based method is proposed along with a correlative webserver to access the proposed
tool.



Firstly, protein features were extracted in a newly feature building way termed, g-gap
tripeptide composition. After which a proposed cascade linear discriminant analysis (Cascade
LDA) is used to alleviate the high dimensional difficulties with the Analysis Of Variance (ANOVA)
as a feature importance criterion. Finally, Support Vector Machine (SVM) is used as the classifier
to identify cancerlectins.



The proposed method achieved an accuracy of 91.34% with sensitivity of 89.89%, specificity
of 92.48% and an 0.8318 Mathew’s correlation coefficient based on only 13 fusion features
in jackknife cross validation, the result of which is superior to other published methods in this domain.



 In this study, a new method based only on primary structure of protein is proposed
and experimental results show that it could be a promising tool to identify cancerlectins. An openaccess
webserver is made available in this work to facilitate other related works.


Liangwei Yang

Papers

Influential Nodes Identification in Complex Networks via Information Entropy

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Federated Social Recommendation with Graph Neural Network

ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

Identification of Cancerlectins By Using Cascade Linear Discriminant Analysis and Optimal g-gap Tripeptide Composition