Argumentation Reasoning with Graph Neural Networks for Reddit Conversation Analysis

The automated analysis of different trends in online debating forums is an interesting tool for sampling the agreement between citizens in different topics. In these online debating forums, users post different comments and answers to previous comments of other users. In previous work, we have defined computational models to measure different values in these online debating forums. A main ingredient in these models has been the identification of the set of winning posts trough an argumentation problem that characterizes this winning set trough a particular argumentation acceptance semantics. In the argumentation problem we first associate the online debate to analyze as a debate tree. Then, comments are divided in two groups, the ones that agree with the root comment of the debate, and the ones that disagree with it, and we extract a bipartite graph where the unique edges are the disagree edges between comments of the two different groups. Once we compute the set of winning posts, we compute the different measures we are interested to get from the debate, as functions defined over the bipartite graph and the set of winning posts. In this work, we propose to explore the use of graph neural networks to solve the problem of computing these measures, using as input the debate tree, instead of our previous argumentation reasoning system that works with the bipartite graph. We focus on the particular online debate forum Reddit, and on the computation of a measure of the polarization in the debate. Our results over a set of Reddit debates, show that graph neural networks can be used with them to compute the polarization measure with an acceptable error, even if the number of layers of the network is bounded by a constant.

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TSI-GNN: Extending Graph Neural Networks to Handle Missing Data in Temporal Settings

Frontiers in Big Data ◽

10.3389/fdata.2021.693869 ◽

2021 ◽

Vol 4 ◽

Author(s):

David Gordon ◽

Panayiotis Petousis ◽

Henry Zheng ◽

Davina Zamanzadeh ◽

Alex A.T. Bui

Keyword(s):

Neural Networks ◽

Missing Data ◽

Bipartite Graph ◽

Message Passing ◽

Representation Learning ◽

Temporal Information ◽

Graph Representation ◽

Sequence Information ◽

Novel Approach ◽

Graph Neural Networks

We present a novel approach for imputing missing data that incorporates temporal information into bipartite graphs through an extension of graph representation learning. Missing data is abundant in several domains, particularly when observations are made over time. Most imputation methods make strong assumptions about the distribution of the data. While novel methods may relax some assumptions, they may not consider temporality. Moreover, when such methods are extended to handle time, they may not generalize without retraining. We propose using a joint bipartite graph approach to incorporate temporal sequence information. Specifically, the observation nodes and edges with temporal information are used in message passing to learn node and edge embeddings and to inform the imputation task. Our proposed method, temporal setting imputation using graph neural networks (TSI-GNN), captures sequence information that can then be used within an aggregation function of a graph neural network. To the best of our knowledge, this is the first effort to use a joint bipartite graph approach that captures sequence information to handle missing data. We use several benchmark datasets to test the performance of our method against a variety of conditions, comparing to both classic and contemporary methods. We further provide insight to manage the size of the generated TSI-GNN model. Through our analysis we show that incorporating temporal information into a bipartite graph improves the representation at the 30% and 60% missing rate, specifically when using a nonlinear model for downstream prediction tasks in regularly sampled datasets and is competitive with existing temporal methods under different scenarios.

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Signed Bipartite Graph Neural Networks

10.1145/3459637.3482392 ◽

2021 ◽

Author(s):

Junjie Huang ◽

Huawei Shen ◽

Qi Cao ◽

Shuchang Tao ◽

Xueqi Cheng

Keyword(s):

Neural Networks ◽

Bipartite Graph ◽

Graph Neural Networks

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Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications

2020 IEEE 36th International Conference on Data Engineering (ICDE) ◽

10.1109/icde48307.2020.00149 ◽

2020 ◽

Author(s):

Zhao Li ◽

Xin Shen ◽

Yuhang Jiao ◽

Xuming Pan ◽

Pengcheng Zou ◽

...

Keyword(s):

Neural Networks ◽

Bipartite Graph ◽

Large Scale ◽

Graph Neural Networks

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Graph Neural Networks for Prediction of Fuel Ignition Quality

10.26434/chemrxiv.12280325.v1 ◽

2020 ◽

Author(s):

Artur Schweidtmann ◽

Jan Rittig ◽

Andrea König ◽

Martin Grohe ◽

Alexander Mitsos ◽

...

Keyword(s):

Neural Networks ◽

Octane Number ◽

Molecular Graph ◽

Chemical Properties ◽

Graph Representation ◽

Structure Property ◽

Oxygenated Hydrocarbons ◽

Physico Chemical ◽

Ignition Quality ◽

Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

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