Ridwan Amure
Biographic Data
| ID | 8547902 |
|---|---|
| NAME | Ridwan Amure |
| GIVEN NAMES | Ridwan |
| FAMILY NAME | Amure |
| SIGNATURE | AMURE R |
| AFFILIATIONS | University of Arkansas at Little Rock |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A comparative evaluation of social network analysis tools
Graphs are increasingly used in research, industry, and government. This has led to a wide range of analytical and graph-processing tools. There are various tools and platforms for graph processing. Over time, diverse systems have emerged, employing various statistics and criteria to evaluate their effectiveness and usability in processing graph data. As a result, comparing the various systems’ performances becomes challenging. This study benchma…
Modeling cross-platform narrative templates
Over the past decade, online social media has grown in size, features, and complexity, providing users with increased satisfaction and prompting many to maintain accounts across multiple platforms. Information actors have also taken advantage of this environment, using cross-platform dynamics to amplify content’s reach and target specific audiences strategically. As these actors will likely continue exploiting social media, we argue that it is cr…
Co-commenters as clues
We propose a semi-supervised approach that finds anomalous YouTube channels by combining co-commenter networks and engagement features. Our method, named SEPS (Semi-Supervised Embedding-based Propagation Scoring), uses a small set of labeled anomalies to guide the learning process. It trains a graph neural network to embed nodes, spreads those partial labels through a simple classification head, and flags channels that appear suspicious. The mode…
Kg-Cfsa
Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data fro…
No prominent works on this page.
Kg-Cfsa
Analyzing opinions, extracting and modeling information, and performing network analysis in online information studies are challenging tasks with multi-source social network data. This complexity arises from the difficulty in harnessing data across various platforms and the absence of a unified data modeling approach. Although social network analysis has used a multiplex approach to study complex networks, no previous work has integrated data fro…
A comparative evaluation of social network analysis tools
Graphs are increasingly used in research, industry, and government. This has led to a wide range of analytical and graph-processing tools. There are various tools and platforms for graph processing. Over time, diverse systems have emerged, employing various statistics and criteria to evaluate their effectiveness and usability in processing graph data. As a result, comparing the various systems’ performances becomes challenging. This study benchma…
Modeling cross-platform narrative templates
Over the past decade, online social media has grown in size, features, and complexity, providing users with increased satisfaction and prompting many to maintain accounts across multiple platforms. Information actors have also taken advantage of this environment, using cross-platform dynamics to amplify content’s reach and target specific audiences strategically. As these actors will likely continue exploiting social media, we argue that it is cr…
Co-commenters as clues
We propose a semi-supervised approach that finds anomalous YouTube channels by combining co-commenter networks and engagement features. Our method, named SEPS (Semi-Supervised Embedding-based Propagation Scoring), uses a small set of labeled anomalies to guide the learning process. It trains a graph neural network to embed nodes, spreads those partial labels through a simple classification head, and flags channels that appear suspicious. The mode…
Complex Network Analysis Techniques (4 works) · Computer Science (4 works) · Social media (3 works) · World Wide Web (3 works) · Data science (2 works) · Graph (2 works) · Sentiment Analysis and Opinion Mining (2 works) · Social network analysis (2 works) · Theoretical Computer Science (2 works) · Advanced Graph Neural Networks (1 work)