A. Munyole

A. Munyole

University of Nairobi

H-index: 3

Africa-Kenya

About A. Munyole

A. Munyole, With an exceptional h-index of 3 and a recent h-index of 3 (since 2020), a distinguished researcher at University of Nairobi, specializes in the field of Social media analysis, fake news detection, AI, NLP.

His recent articles reflect a diverse array of research interests and contributions to the field:

ConeE: Global and local context-enhanced embedding for inductive knowledge graph completion

CAF-ODNN: Complementary attention fusion with optimized deep neural network for multimodal fake news detection

Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field

Rumor Detection with Supervised Graph Contrastive Regularization

Dual emotion based fake news detection: A deep attention-weight update approach

Graph contrastive learning with feature augmentation for rumor detection

Graph Contrastive ATtention Network for Rumor Detection

A. Munyole Information

University

University of Nairobi

Position

Web Technologies

Citations(all)

69

Citations(since 2020)

69

Cited By

1

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

University of Nairobi

A. Munyole Skills & Research Interests

Social media analysis

fake news detection

AI

NLP

Top articles of A. Munyole

ConeE: Global and local context-enhanced embedding for inductive knowledge graph completion

Authors

Jingchao Wang,Weimin Li,Fangfang Liu,Zhenhai Wang,Alex Munyole Luvembe,Qun Jin,Quanke Pan,Fangyu Liu

Journal

Expert Systems with Applications

Published Date

2024/7/15

Knowledge graph completion (KGC) aims at completing missing information in knowledge graphs (KGs). Most previous works work well in the transductive setting, but are not applicable in the inductive setting, i.e., test entities can be unseen during training. Recently proposed methods obtain inductive ability by learning logic rules from subgraphs. However, all these works only consider the structural information of subgraphs while ignoring the rich contextual semantic information underlying KGs, which tends to lead to a sub-optimal embedding result. Furthermore, they tend to perform poorly when the subgraphs are sparse. To address these problems, we propose a global and local Context-enhanced Embedding network, ConeE, which can fully utilize local and global contextual information to enhance embedding representations through the following two components. (1) The global context modeling module …

CAF-ODNN: Complementary attention fusion with optimized deep neural network for multimodal fake news detection

Authors

Alex Munyole Luvembe,Weimin Li,Shaohau Li,Fangfang Liu,Xing Wu

Journal

Information Processing & Management

Published Date

2024/5/1

Fake news is a real problem; unfortunately, it seems to worsen. Even though some false news detection methods have made significant progress, current multimodal approaches integrate cross-modal features directly without considering uncorrelated semantic representations may introduce noise into the multimodal features. This phenomenon reduces model accuracy by obscuring subtle differences between text and images crucial for identifying fake news. Uncorrelated semantics also reduce the detection accuracy since the identification often relies on these subtle differences. To address these challenges, we propose a unified Complementary Attention Fusion with an Optimized Deep Neural Network (CAF-ODNN) that captures subtle cross-modal relationships for multimodal fake news detection. CAF introduces image captioning to represent images semantically, allowing bidirectional complementary attention …

Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

Authors

Yan Zhao,Weimin Li,Fangfang Liu,Jingchao Wang,Alex Munyole Luvembe

Journal

Expert Systems with Applications

Published Date

2024/3/15

Community detection aims to discover hidden communities or groups in complex networks and is essentially unsupervised clustering behavior. However, most of the existing unsupervised methods are designed for homogeneous networks; therefore, they cannot effectively handle heterogeneous structures and rich semantic information. Under such a situation, it is difficult to accurately detect communities in heterogeneous networks that better reflect the real world. Therefore, this work aims to design an unsupervised framework to fuse heterogeneous structure information and interpret the rich semantics of the network in the form of community semantics. Thus, a heterogeneous network community detection method, called HAESF, is introduced. It includes two modules: the Heterogeneous Auto-Encoder (HAE) and the Semantic Factorization (SF) modules. In more detail, the HAE module adopts a hierarchical attention …

Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field

Authors

Chang Guo,Weimin Li,Jingchao Wang,Xiao Yu,Xiao Liu,Alex Munyole Luvembe,Can Wang,Qun Jin

Journal

Knowledge-Based Systems

Published Date

2024/2/27

Heterogeneous networks, like social and academic networks are widespread in the real world, characterized by diverse nodes and complex relationships. Influence maximization is a crucial research topic, in these networks, as it can help in comprehending the mechanisms of information propagation and diffusion. Effectively utilizing complex structural information poses a challenge in current research on influence maximization in heterogeneous information networks. As a solution to this problem, a heterogeneous network influence maximization algorithm based on the multi-scale propagation strength and repulsive force of propagation field is proposed. Firstly, based on the propagation field, we design a multi-scale propagation strength index for the propagation ability of nodes to achieve maximum coverage of influence propagation. Specifically, in the homogeneous structure, the homogeneous propagation …

Rumor Detection with Supervised Graph Contrastive Regularization

Authors

Shaohua Li,Weimin Li,Alex Munyole Luvembe,Weiqin Tong

Published Date

2023/11/20

The rapid spread of rumors on social networks can significantly impact social stability and people’s daily lives. Recently, there has been increasing interest in rumor detection methods based on feedback information generated during user interactions and the propagation structure. However, these methods often face the challenge of limited labeled data. While addressing data dependency issues, graph-based contrastive learning methods struggle to effectively represent different samples of the same class in supervised classification tasks. This paper proposes a novel Supervised Graph Contrastive Regularization (SGCR) approach to tackle these complex scenarios. SGCR leverages label information for supervised contrastive learning and applies simple regularization to the embeddings by considering the variance of each dimension separately. To prevent the collapse problem, sessions belonging to the same …

Dual emotion based fake news detection: A deep attention-weight update approach

Authors

Alex Munyole Luvembe,Weimin Li,Shaohua Li,Fangfang Liu,Guiqiong Xu

Journal

Information Processing & Management

Published Date

2023/7/1

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic …

Graph contrastive learning with feature augmentation for rumor detection

Authors

Shaohua Li,Weimin Li,Alex Munyole Luvembe,Weiqin Tong

Journal

IEEE Transactions on Computational Social Systems

Published Date

2023/5/4

While online social media brings convenience to people’s communication, it has also caused the widespread spread of rumors and brought great harm. Recent deep-learning approaches attempt to identify rumors by engaging in interactive user feedback. However, the performance of these models suffers from insufficient and noisy labeled data. In this article, we propose a novel rumor detection model called graph contrastive learning with feature augmentation (FAGCL), which injects noise into the feature space and learns contrastively by constructing asymmetric structures. FAGCL takes user preference and news embedding as the initial features of the rumor propagation tree and then adopts a graph attention network to update node representations. To obtain the graph-level representation for rumor classification, FAGCL fuses multiple pooling techniques. Moreover, FAGCL adopts graph contrastive learning as …

Graph Contrastive ATtention Network for Rumor Detection

Authors

Shaohua Li,Weimin Li,Alex Munyole Luvembe,Weiqin Tong

Published Date

2023/11/20

Detecting rumors from the vast amount of information in online social media has become a formidable challenge. Rumor detection based on rumor propagation trees benefits from crowd wisdom and has become an important research method for rumor detection. However, node representations in such methods rely on limited label information and lose a lot of node information when obtaining graph-level representations through pooling. This paper proposes a novel rumor detection model called Graph Contrastive ATtention Network (GCAT). GCAT adopts a graph attention model as the encoder, applies graph self-supervised learning without negative label pairs as an auxiliary task to update network parameters, and combines multiple pooling techniques to obtain the graph-level representation of the rumor propagation tree. To verify the effectiveness of our model, we conduct experiments on two real-world datasets …

Influence maximization algorithm based on Gaussian propagation model

Authors

WeiMin Li,Zheng Li,Alex Munyole Luvembe,Chao Yang

Journal

Information Sciences

Published Date

2021/8/1

The influence of each entity in a network is a crucial index of the network information dissemination. Greedy influence maximization algorithms suffer from time efficiency and scalability issues. In contrast, heuristic influence maximization algorithms improve efficiency, but they cannot guarantee accurate results. Considering this, this paper proposes a Gaussian propagation model based on the social networks. Multi-dimensional space modeling is constructed by offset, motif, and degree dimensions for propagation simulation. This space’s circumstances are controlled by some influence diffusion parameters. An influence maximization algorithm is proposed under this model, and this paper uses an improved CELF algorithm to accelerate the influence maximization algorithm. Further, the paper evaluates the effectiveness of the influence maximization algorithm based on the Gaussian propagation model supported by …

See List of Professors in A. Munyole University(University of Nairobi)

A. Munyole FAQs

What is A. Munyole's h-index at University of Nairobi?

The h-index of A. Munyole has been 3 since 2020 and 3 in total.

What are A. Munyole's top articles?

The articles with the titles of

ConeE: Global and local context-enhanced embedding for inductive knowledge graph completion

CAF-ODNN: Complementary attention fusion with optimized deep neural network for multimodal fake news detection

Integrating heterogeneous structures and community semantics for unsupervised community detection in heterogeneous networks

Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field

Rumor Detection with Supervised Graph Contrastive Regularization

Dual emotion based fake news detection: A deep attention-weight update approach

Graph contrastive learning with feature augmentation for rumor detection

Graph Contrastive ATtention Network for Rumor Detection

...

are the top articles of A. Munyole at University of Nairobi.

What are A. Munyole's research interests?

The research interests of A. Munyole are: Social media analysis, fake news detection, AI, NLP

What is A. Munyole's total number of citations?

A. Munyole has 69 citations in total.

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