INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks

Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out as an area that could greatly benefit from the incorporation of dynamic network representation due to the ongoing development of customer-brand relationships. To elaborate this idea, we introduce INFLECT-DGNN, a new framework for INFLuencer prEdiCTion with Dynamic Graph Neural Networks that combines Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) with weighted loss functions, the Synthetic Minority Oversampling TEchnique (SMOTE) adapted for graph data, and a carefully crafted rolling-window strategy. To evaluate predictive performance, we utilize a unique corporate data set with networks of three cities and derive a profit-driven evaluation methodology for influencer prediction. Our results show how using RNN to encode temporal attributes alongside GNNs significantly improves predictive performance. We compare the results of various models to demonstrate the importance of capturing graph representation, temporal dependencies, and using a profit-driven methodology for evaluation.Comment: 26 pages, 10 figure

Benchmarking conventional outlier detection methods

Nowadays, businesses in many industries face an increasing flow of data and information. Data are at the core of the decision-making process, hence it is vital to ensure that the data are of high quality and no noise is present. Outlier detection methods are aimed to find unusual patterns in data and find their applications in many practical domains. These methods employ different techniques, ranging from pure statistical tools to deep learning models that have gained popularity in recent years. Moreover, one of the most popular outlier detection techniques are machine learning models. They have several characteristics which affect the potential of their usefulness in real-life scenarios. The goal of this paper is to add to the existing body of research on outlier detection by comparing the isolation forest, DBSCAN and LOF techniques. Thus, we investigate the research question: which ones of these outlier detection models perform best in practical business applications. To this end, three models are built on 12 datasets and compared using 5 performance metrics. The final comparison of the models is based on the McNemar’s test, as well as on ranks per performance measure and on average. Three main conclusions can be made from the benchmarking study. First, the models considered in this research disagree differently, i.e. their type I and type II errors are not similar. Second, considering the time, AUPRC and sensitivity metrics, the iForest model is ranked the highest. Hence, the iForest model is the best in the cases when time performance is a key consideration as well as when the opportunity costs of not detecting an outlier are high. Third, the DBSCAN model obtains the highest ranking along the F1 score and precision dimensions. That allows us to conclude that if raising many false alarms is not an important concern, the DBSCAN model is the best to employ.</p

Explainable Learning Analytics: assessing the stability of student success prediction models by means of explainable AI

Beyond managing student dropout, higher education stakeholders need decision support to consistently influence the student learning process to keep students motivated, engaged, and successful. At the course level, the combination of predictive analytics and self-regulation theory can help instructors determine the best study advice and allow learners to better self-regulate and determine how they want to learn. The best performing techniques are often black-box models that favor performance over interpretability and are heavily influenced by course contexts. In this study, we argue that explainable AI has the potential not only to uncover the reasons behind model decisions, but also to reveal their stability across contexts, effectively bridging the gap between predictive and explanatory learning analytics (LA). In contributing to decision support systems research, this study (1) leverages traditional techniques, such as concept drift and performance drift, to investigate the stability of student success prediction models over time; (2) uses Shapley Additive explanations in a novel way to explore the stability of extracted feature importance rankings generated for these models; (3) generates new insights that emerge from stable features across cohorts, enabling teachers to determine study advice. We believe this study makes a strong contribution to education research at large and expands the field of LA by augmenting the interpretability and explainability of prediction algorithms and ensuring their applicability in changing contexts.</p