# KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction

Ching-Hao Fan, Hao Zhou, Yao Sun, Geovanny Palomino Roldan, Olga Kokshagina, Marc Santolini, Lijing Wang

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## To cite this version:

Ching-Hao Fan, Hao Zhou, Yao Sun, Geovanny Palomino Roldan, Olga Kokshagina, et al., KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction. ICMR '25: International Conference on Multimedia Retrieval, Jun 2025, Chicago IL USA, United States. pp.275-283, 10.1145/3731715.3733368. hal-05176637

HAL Id: hal-05176637

https://hal.science/hal-05176637v1

Submitted on 22 Jul 2025

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# KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction

Ching-Hao Fan

New Jersey Institute of Technology
Newark, NJ, USA
cf322@njit.edu

Hao Zhou

New Jersey Institute of Technology
Newark, NJ, USA
hz394@njit.edu

Yao Sun

New Jersey Institute of Technology
Newark, NJ, USA
yao.sun@njit.edu

Geovanny Palomino Roldan

New Jersey Institute of Technology
Newark, NJ, USA
gp336@njit.edu

Olga Kokshagina

The University of Sydney
Sydney, Australia
Learning Planet Institute
Research Unit Learning Transitions
(UR LT, joint unit with CY Cergy
Paris University)
Paris, France
olga.kokshagina@sydney.edu.au

Marc Santolini

Sorbonne Université
CNRS, ERL U1338 Inserm
Paris, France
Learning Planet Institute
Research Unit Learning Transitions
(UR LT, joint unit with CY Cergy
Paris University)
Paris, France
marc.santolini@inserm.fr

Lijing Wang

New Jersey Institute of Technology
Newark, NJ, USA
lw29@njit.edu

## Abstract

Accurate user engagement prediction is critical to the success of crowdsourcing in online communities. This study focuses on analyzing user behavior in crowd-driven platforms, addressing the challenge of predicting engagement levels based on behavioral dynamics in online knowledge-sharing environments. We propose a comprehensive framework, Knowledge-Enhanced Graph Neural Networks (KEGNN), which begins by collecting and refining user activity data from a real-world platform and quantifying user engagement using the RFE (Recency, Frequency, Engagement) model to extract meaningful insights from the comprehensive record of the user's behavior. KEGNN leverages a graph neural network (GNN)-based architecture to capture temporal and spatial patterns in user dynamics, incorporating user-generated textual knowledge to enhance prediction performance. Extensive experiments on data from Just One Giant Lab (JOGL), an open knowledge-sharing platform, demonstrate that KEGNN outperforms adapted baseline models originally designed for other domains. The results highlight its effectiveness in predicting short- and long-term user engagement. Our framework provides a robust foundation for analyzing online user behavior and holds significant potential for adaptation to similar challenges across various domains. The source code is publicly available at: https://github.com/charliefanfan/ICMR-GNN

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This work is licensed under a Creative Commons Attribution 4.0 International License.
ICMR '25, Chicago, IL, USA

© 2025 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-1877-9/2025/06
https://doi.org/10.1145/3731715.3733368

## CCS Concepts

* **Computing methodologies** → **Neural networks; Information extraction;** • **Information systems** → **Social networks.**

## Keywords

knowledge sharing, user engagement prediction, graph learning, graph neural networks, natural language processing

## ACM Reference Format:

Ching-Hao Fan, Hao Zhou, Yao Sun, Geovanny Palomino Roldan, Olga Kokshagina, Marc Santolini, and Lijing Wang. 2025. KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction. In *Proceedings of the 2025 International Conference on Multimedia Retrieval (ICMR '25)*, June 30-July 3, 2025, Chicago, IL, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3731715.3733368

## 1 Introduction

Crowdsourcing, as one type of online community, has been viewed as an effective way to generate novel ideas and solutions across numerous areas [1, 37]. It relies on a self-identification process among capable participants who are willing to contribute their knowledge to help complete the task [22]. In such crowdsourcing communities, registered users selectively devote their time and energy to projects that they find promising, and their contributions often determine whether the projects can be successful. As such, accurately delineating and predicting users' behavioral patterns becomes crucial to efficient and successful crowdsourcing [18, 24, 26].

As one of the most important types of user behavior in crowd-empowered online communities, knowledge sharing is configured by the profound impacts of time and the social disembodiment
<figure><img src="image_5.png" /><figcaption>Figure 1: The concept and framework of KEGNN. KEGNN collects and refines dynamic and static data to extract TS features, knowledge-sharing textual content, and dynamic networks, and then calculates RFE scores to quantify user engagement. A dynamic graph is built using the following-follower network with nodes associated with refined features $x^{TS}, x^{KS}$. The GNN-based model embeds $x^{TS}$ through an RNN module and $x^{KS}$ through an NLP module, which are then combined and propagated via a GNN. The final prediction $\hat{y}$ is made using an MLP.</figcaption></figure>

of contributed ideas [16, 31]. However, how knowledge sharing influences contributors' engagement and other behaviors remains unclear. In this paper, we explore user behavior analysis within crowd-powered communities, focusing on predicting user engagement levels based on behavior dynamics in an online knowledge-sharing platform. We frame this research problem as a prediction task.

There are several challenges regarding this task. First, users on online platforms engage in diverse activities such as following others, creating posts, and browsing content, resulting in comprehensive but complex behavioral records. Assessing a user's engagement level across these varied actions is inherently challenging, complicating the prediction of user engagement levels. Second, online sharing platforms inherently have graph structures based on user interactions with each other over time, leading to complex temporal and spatial dynamics. Traditional models often struggle to capture the complex, dynamic, and interdependent nature of interactions within these platforms. Additionally, vast amounts of textual information are generated and shared among users, providing valuable information that can enhance the accuracy of user behavior predictions. Extracting semantic patterns from this textual data further complicates the learning process. Despite these challenges, no existing work has developed a predictive model that effectively integrates such multifaceted user dynamics for accurately predicting user engagement.

In this work, we propose a novel framework, KEGNN (Knowledge-Enhanced Graph Neural Networks), to analyze and predict user behavior dynamics. Our framework collects and refines user activity data from a real-world online platform. We adopt an RFE (Recency, Frequency, Engagement) model to quantify user engagement based on interactions and behaviors. A graph neural network (GNN)-based model is then designed to capture temporal and spatial patterns in user dynamics. To integrate knowledge-sharing dynamics into GNN learning, we incorporate a natural language

processing (NLP) module to extract semantic patterns from user-generated textual content. Our main contributions are summarized below:

* We formulate the user behavior analysis problem in crowd-empowered online communities as a user engagement prediction task, categorizing users as active or inactive based on a quantified engagement level. To the best of our knowledge, we are the first to quantify user engagement from complex behavior dynamics.

* We design the KEGNN framework, which encompasses data collection, processing, model learning, prediction, and evaluation, and is adaptable to other domains.

* We propose a GNN-based model to capture the temporal and spatial patterns of user behavior dynamics within a dynamic graph structure, reflecting user relationships inherent in knowledge-sharing activities. KEGNN also integrates user-generated textual knowledge into GNN learning through an NLP module, enhancing prediction performance.

* We perform extensive experiments on a real-world online knowledge-sharing platform. The results demonstrate that our model significantly outperforms adapted baseline models originally designed for other domains, validating its effectiveness in capturing user behavior patterns for accurate engagement prediction.

# 2 Related work

## 2.1 User behavior analysis

User behavior refers to the actions that users take while using a product or service. It is usually studied for product development [13] and increasingly for predictive modeling using NLP. In online social networks, behavior analysis is often employed to connect advertisements with users who share similar interests [30]. Recently, user behavior analysis has aided marketing and decision-making
in the fashion industry, though its applications extend beyond [45]. Some studies integrate human emotions and sentiments into statistical models to study social influence on social networks [7]. Given its significance, numerous studies have explored user behavior by predicting the engagement level of social media posts [3, 11, 17, 41]. However, these efforts primarily focus on Twitter platform, quantifying engagement of a tweet's post through user activities on tweets. Notably, previous research has not directly quantified user engagement from comprehensive records of user's behavior. In contrast, our work defines a specific problem of user behavior analysis. User engagement prediction is one such new problem that has not yet been explored.

## 2.2 Online knowledge sharing

Online knowledge-sharing platforms have introduced concepts such as online crowdsourcing, defined as an online activity where individuals, companies, or institutions propose specific tasks that involve mutual benefits [14]. The quality of subsequent contributions in such platforms largely depends on how the online crowd participates, as this influences the idea formation process [28]. Additionally, various factors motivate users to contribute; for instance, view counts generate positive feelings and encourage active participation, while posted comments foster a two-way process of social interaction [34]. Integrating knowledge sharing into user behavior analysis is crucial and urgent because it is a key indicator of user engagement within a community, leading to better prediction accuracy.

## 2.3 DNN Methods in studying online behaviors

Deep neural network (DNN) models have been widely applied across various domains, demonstrating their versatility and effectiveness. In particular, GNN models have been successfully applied to solve problems in various fields, such as epidemic forecasting [5, 12, 15, 42], traffic forecasting [6, 38], recommendation systems [27], stock market prediction [44, 46] and supply chain forecasting [2, 40]. These methods are highly effective due to their ability to incorporate neighboring node representations and consider both node information and the topological structure of the graph. For instance, text classifiers have effectively utilized these features to enhance prediction accuracy [36]. Moreover, a graph partition approach based on DNNs has been applied not only to analyze statistical features but also semantic content and emotions, proving useful for tasks such as fake review detection and behavioral predictions [32]. GNNs have also been applied to predict a social media post's engagement level by leveraging graph-based features to analyze user behavior [4, 29]. While this is closely related to our research problem, it differs in that prior studies focus on post-level engagement rather than directly modeling user engagement. Moreover, previous work has not utilized GNNs specifically for predicting a user's engagement. In contrast, our work defines a distinct problem of user behavior analysis by integrating comprehensive records of user behavior, graph-based features, and user-generated textual knowledge. We propose a GNN-based solution to address this unique challenge.

# 3 Method

## 3.1 Problem Formulation

We formulate the user behavior prediction problem as a graph-based classification problem. We consider each user as a node, so we have $N$ nodes (users) in total. Each node is associated with two types of features: 1) historical user activity statistics in time-series (TS) format denoted as $x^{TS}$, and 2) knowledge sharing among users in free text format denoted as $x^{KS}$. More specifically, at time point $t$, a node $i$'s TS feature includes this user's historical RFE<fnref n="1" /> (Recency, Frequency, Engagement) scores and comments/posts counts in the past $T$ time periods denoted as $x_{i,t}^{TS} \in \mathbb{R}^T$. The node's knowledge feature $x_{i,t}^{KS} \in \mathbb{R}^C$ is a set of comments and posts generated by the user during time period $t$. $C$ denotes the text sequence length. A graph is constructed based on the following/follower network $A_t \in \mathbb{R}^{N \times N}$. To dynamically capture the evolving relationships between users over time, $A_t$ is updated at each time point based on the dynamic change of the following/follower relationship. The objective is to predict every user's engagement (active or inactive) at a future time point. We aim to develop a predictor $f$ that forecasts a user's RFE at a future time $t+h$, represented by $o_{t+h}$, with $h$ representing the prediction horizon. At time $t$, let $X_t^{TS} = [x_{1,t}^{TS}, ..., x_{N,t}^{TS}]$, $X_t^{KS} = [x_{1,t}^{KS}, ..., x_{N,t}^{KS}]$, forecasting for $N$ users using $f$ is represented as:

$$ \hat{o}_{t+h} = f(X_t^{TS}, X_t^{KS}, \theta) \quad (1) $$

where $\theta$ denotes parameters of the predictor and $\hat{o}_{t+h} \in \mathbb{R}^N$ denotes the prediction of $o_{t+h} \in \mathbb{R}^N$.

The ground truth $o_{t+h}$ and predicted $\hat{o}_{t+h}$ values will be further classified as active (1) or inactive (0) based on a pre-set threshold $\tau$, where active users conduct more activities (higher RFE value) in social media than inactive users. Let $y_{t+h}$ denote the ground truth engagement of a user that $y_{t+h} = 1$ means active and $y_{t+h} = 0$ means inactive, $\hat{y}_{t+h}$ denote the classified prediction, then the classification of $N$ users $\hat{y}_{t+h} = [\hat{y}_{1,t+h}, ..., \hat{y}_{N,t+h}]$ is based on:

$$ \hat{y}_{i,t+h} = \begin{cases} 1, & \text{if } \hat{o}_{i,t+h} \ge \tau \\ 0, & \text{otherwise} \end{cases} \quad (2) $$

where $\hat{y}_{t+h} \in \mathbb{R}^N$ denotes the prediction of $y_{t+h} \in \mathbb{R}^N$. The important notations are summarized in Table 1.

The two-stage problem formulation—regression for RFE scores followed by threshold-based classification—allows greater flexibility in analyzing user engagement across different scenarios. By adjusting the threshold, we can easily tailor the classification without the need for retraining or re-predicting, enabling more dynamic and context-specific analysis.

## 3.2 Framework Overview

To address the problem described in 3.1, we propose KEGNN, with the concept and framework shown in Figure 1. We collect both dynamic (e.g., following/follower relationships, comments, posts) and static (e.g., user profiles, project profiles) information from the online platform. In the data refining step, we extract TS features, knowledge-sharing textual content features, and dynamic networks from the raw data. We then calculate RFE scores based on the refined records to quantify user engagement using the RFE model.

<footnote n="1">¹The details of RFE model are described in Section 3.3</footnote>
<table><caption>**Table 1: Important notations and their descriptions**</caption><thead><tr><th>Notation</th><th>Description</th></tr></thead><tbody><tr><td>N</td><td>Total number of users</td></tr><tr><td>T</td><td>Historical window size</td></tr><tr><td>h</td><td>Prediction horizon</td></tr><tr><td>C, B</td><td>NLP sequence length, embedding dimension</td></tr><tr><td>D<sub>1</sub>, D<sub>2</sub>, F<sup>l</sup></td><td>Hidden dimensions</td></tr><tr><td>L<sub>rnn</sub>, L<sub>gnn</sub></td><td>Number of hidden layers</td></tr><tr><td>A<sub>t</sub> ∈ ℝ<sup>N×N</sup></td><td>Following/follower network at time t</td></tr><tr><td>X<sup>TS</sup><sub>t</sub> ∈ ℝ<sup>N×T</sup></td><td>Time series of user activity statistics at time t</td></tr><tr><td>X<sup>KS</sup><sub>t</sub> ∈ ℝ<sup>N×C</sup></td><td>Textual knowledge at time t</td></tr><tr><td>o<sub>t+h</sub>, ˙o<sub>t+h</sub> ∈ ℝ<sup>N</sup></td><td>RFE scores of N users at time t + h</td></tr><tr><td>y<sub>t+h</sub>, ŷ<sub>t+h</sub> ∈ {0, 1}<sup>N</sup></td><td>Engagement level of N users at time t + h</td></tr></tbody></table>

A dynamic graph is constructed based on the following-follower network, with nodes associated with the aforementioned features. To capture temporal patterns from TS features, we use a Recurrent Neural Network (RNN) for TS feature embedding. To incorporate users' knowledge-sharing behavior into the graph learning process, a NLP module is designed to extract semantic information from users' comments and posts, which are then transformed into semantic embeddings. These TS and semantic embeddings are combined and propagated through the dynamic graph using a GNN module. The updated embeddings are fed into an output module (Multi-Layer Perceptron, MLP) for the final prediction. More details are provided in the following.

## 3.3 Spatio-temporal Data Extraction

On a social media platform, users engage in various activities, such as registering an account, following others, liking posts, commenting, or browsing pages. These actions are typically tracked by the platform, creating behavioral trajectories for each user. Based on the problem formulation, we need to extract temporal and spatial features from these trajectories to build the dynamic graph. We divide the timeline of users' trajectories into discrete time intervals (e.g., weekly durations). Given N users and M time intervals in our dataset, we extract temporal and spatial features using the following methods.

**Temporal features.** In each time duration t, we first collect the comments and posts generated during t for every user, forming x<sup>KS</sup>. This free-text information is crucial for learning semantic patterns of knowledge-sharing behavior. Next, based on the timestamps of each comment and post, we extract a time series of comment/post counts for each user, which forms part of the TS features for the nodes. Finally, the user's engagement level is quantified as an RFE score and processed as a time series of engagement levels. The comment/post count time series and engagement level time series together form the TS features x<sup>TS</sup>.

**Engagement level measurement.** It is not easy to measure the user's engagement level from high dimensions of trajectories leading to difficulties in identifying the most contributing users. In our framework, we propose an RFE (Recency, Frequency, Engagement) model, which is a variant of the RFM (Recency, Frequency, Monetary) model [8], to evaluate user engagement by calculating

RFE scores based on user interactions and engagement behaviors. The RFE model consists of three components: Recency (R), Frequency (F), Engagement (E). In each time duration t, the R, F, and E scores are calculated separately according to the following definitions:

* **Recency (R)** measures a user's most recent activity time which can be used to understand the user's retention. It is calculated as the number of days from the most recent activity date to the current date (i.e., the end day of time duration t).

* **Frequency (F)** measures the distinct days of users conducting any activities within the time duration t. The F score can be used to understand the user's dependence and loyalty to the social media platform.

* **Engagements (E)** measures the degree of user interactions on the platform which can be used to understand the user's interest and participation in the platform content. It is calculated as the total number of activities made by a user within time duration t.

To mitigate the impact of extreme values caused by users who may be overly active due to roles such as administration, or inactive for extended periods, we apply a logarithmic transformation to the R, F, and E scores. These transformed scores are then rescaled into discrete engagement levels within the range [1,2,3,4,5]. The scaling of each score is based on the distribution of that score across all users.

The RFE score for each user is calculated by:

$$S = aR + bF + cE \quad (3)$$

where a, b, c are the weights for R, F, and E, determining how much of each score is considered in the final RFE value, and a + b + c = 1. A higher RFE score represents a higher engagement level. Each user receives an RFE score at every time point t, resulting in a time series. This allows us to track user's engagement level over time and make predictions of future behavior.

**Spatial features.** At each time step t, we extract following and follower activities based on their time stamps, and build a user interaction network A<sub>t</sub> ∈ ℝ<sup>N×N</sup>, resulting in an M × N × N matrix represents a dynamic graph, where there are N nodes and each node is associated with x<sup>TS</sup>, x<sup>KS</sup>.

## 3.4 KEGNN Model

In KEGNN framework, we develop a GNN-based model combined with RNN and NLP modules to learn patterns from the dynamic graph built in Section 3.3. We describe the details of each module in the following sections.

### 3.4.1 RNN for learning temporal patterns of user behavior.
We learn TS embedding for each user using a Recurrent Neural Network (RNN) [43]. The RNN module can be replaced by Gated Recurrent Unit (GRU) [9] or Long short-term memory (LSTM) [21]. The historical TS of RFE and comment/post counts are normalized using Min-Max normalization and combined together into one time series. At time t, given a multi-user input data x<sup>TS</sup><sub>t</sub> = [x<sup>TS</sup><sub>1,t</sub>, ..., x<sup>TS</sup><sub>N,t</sub>], we transform the matrix to multi-step input data x<sup>TS</sup><sub>t</sub> = [x<sup>TS</sup><sub>t-T+1</sub>, ..., x<sup>TS</sup><sub>k</sub>, ..., x<sup>TS</sup><sub>t</sub>] ∈ ℝ<sup>T×N</sup> where x<sup>TS</sup><sub>k</sub> ∈ ℝ<sup>N</sup> is the
feature of N users at historical time step k. We employ a global RNN of Lrnn hidden layers to capture the temporal interactions among all users. For N users at time t, the RNN embedding $\mathbf{H}_t^{TS}$ is obtained by recurrently performing the following calculation on every historical time steps:

$$ \mathbf{H}_k^{TS} = \tanh(W_{rnn}\mathbf{x}_k^{TS} + U_{rnn}\mathbf{H}_{k-1}^{TS} + \mathbf{b}_{rnn}) \in \mathbb{R}^{D_1 \times N}, \quad (4) $$

where $D_1$ is the dimension of the hidden state; $W_{rnn} \in \mathbb{R}^{D_1 \times N}$, $U_{rnn} \in \mathbb{R}^{D_1 \times D_1}$, $\mathbf{b}_{rnn} \in \mathbb{R}^{D_1}$ are parameters of RNN layers.

### 3.4.2 NLP for learning semantic patterns of knowledge sharing behavior.
A user i in time duration t is associated with a combined knowledge paragraph, i.e., $\mathbf{x}_{i,t}^{KS}$, which is further cleaned by removing noise such as punctuation, special characters, and stop words. It also removes unnecessary information such as URLs, web pages, and special emojis commonly used in social media. For each time duration t, a vocabulary of size V is built based on $\mathbf{X}_t^{KS}$. Then it conducts tokenization, where text is split into smaller tokens like words or subwords. Padding is applied after tokenization to ensure that all sequences have the same length C for batch processing, denoted as $\mathbf{x}_{i,t}^{KS} = [w_1, ..., w_C]$ where $w_i \in \mathbb{R}^V$ represents the i-th token in the sequence. Finally, the padded input is passed through an embedding layer where tokens are converted into dense vector representations resulting in a three-dimensional tensor.

For $\mathbf{X}_t^{KS}$, its embedding is computed as:

$$ \mathbf{E}_t^{KS} = (W_{nlp})^T \mathbf{X}_t^{KS} \in \mathbb{R}^{B \times N \times C}, \quad (5) $$

where $W_{nlp} \in \mathbb{R}^{V \times B}$ is the embedding matrix, B is the embedding dimension, $(\cdot)^T$ means transpose.

The output of the embedding layer $\mathbf{E}_t^{KS}$ is then fed into a bidirectional LSTM (BiLSTM) [39] layer to process the sequence of these embeddings, capture temporal dependencies and long-range relationships between tokens in both forward and backward directions:

$$ \mathbf{H}_t^{KS} = \text{BiLSTM}(\mathbf{E}_t^{KS}, \theta_{lstm}) \in \mathbb{R}^{D_2 \times N}, \quad (6) $$

where $\theta_{lstm}$ represents the parameter set of BiLSTM layers; $D_2$ is the hidden state dimension.

### 3.4.3 GNN for incorporating knowledge and behavior interaction.
After learning the hidden temporal patterns of user behavior $\mathbf{H}_t^{TS}$ and the hidden semantic patterns of user knowledge $\mathbf{H}_t^{KS}$, we combine the two hidden embedding into one embedding vector and design a knowledge propagation model using GNNs. GNNs can effectively model the interdependencies of user behaviors, allowing for a deeper understanding of how knowledge and behaviors propagate through the network. In GNN module, the nodes' hidden feature vectors are iteratively updated from their neighbors, which is similar to knowledge sharing process. We leverage GCN [23] for graph convolution. It consists multiple hidden layers. Let $L_{gnn}$ represents the total number of GCN layers, $F^l$ represents the hidden dimension of the l-th layer, $\mathbf{H}_t^l \in \mathbb{R}^{N \times F^l}$ denotes the matrix of hidden states from the l-th GCN layer:

$$ \mathbf{H}_t^l = g(W_{gnn}^l \mathbf{H}_t^{l-1} A_t + \mathbf{b}_{gnn}^l) \in \mathbb{R}^{F^l \times N}, \quad (7) $$

where $W_{gnn}^l \in \mathbb{R}^{F^l \times F^{l-1}}$, $\mathbf{b}_{gnn}^l \in \mathbb{R}^{F^l}$ are model parameters. g is rectified linear units (ReLU) [35]. $\mathbf{H}_t^l = \mathbf{H}_t^{TS} \parallel \mathbf{H}_t^{KS} \in \mathbb{R}^{(D_1+D_2) \times N}$ is the input of the first GCN layer which concatenates the hidden

embedding from TS features and semantic features. After propagating $L_{gnn}$ times over the graph, we obtain GNN embedding $\mathbf{H}_t^{GNN}$. As described in Section 3.1, since the dynamic graph A evolves over time, our GNN module is designed as a dynamic graph learning process.

### 3.4.4 MLP for making predictions.
The output from GNN module is fed to an MLP module for final prediction:

$$ \hat{\mathbf{o}}_{t+h} = \phi \left( (\mathbf{H}_t^{GNN})^T W_{mlp} + b_{mlp} \right) \in \mathbb{R}^N, \quad (8) $$

where $W_{mlp} \in \mathbb{R}^{F^l}$, $b_{mlp} \in \mathbb{R}$ are parameters, $\phi$ is an identity function, and $\hat{\mathbf{o}}$ denotes the predicted target for N users at time $t+h$. The final prediction $\hat{\mathbf{y}}$ is determined by:

$$ \hat{\mathbf{y}}_{t+h} = I(\hat{\mathbf{o}}_{t+h}, \tau), \quad (9) $$

where $I(\cdot, \cdot)$ is Equation 2.

## 3.5 Optimization

We optimize a $\ell_2$-norm loss function plus a L2 regularization via gradient descent:

$$ \mathcal{L}(\theta) = \|\mathbf{o}_{t+h} - \hat{\mathbf{o}}_{t+h}\|^2 + \lambda \|\theta\|^2, \quad (10) $$

where $\mathbf{o}$ represents the ground truth RFE values of N users; $\hat{\mathbf{o}}$ is the prediction of $\mathbf{o}$. We do not distinguish users in calculating loss. We train our model using $\hat{\mathbf{o}}_{t+h}$ for regression rather than $\hat{\mathbf{y}}_{t+h}$ for classification, allowing performance evaluation with adjustable threshold $\tau$ without affecting the training process.

# 4 Experiment

## 4.1 Data

We collect our dataset from JOGL$^2$, an open platform where users register to join projects, collaborate, and request assistance [19, 20, 33]. The dataset covers daily records from April 13, 2020, to July 12, 2021, and includes information related to projects, users, and user activities within JOGL. Since our goal is to predict users' future engagement based on their spatial and temporal user activities, we select some key information from the raw dataset including user profiles, the contents and timestamps of user generated posts and comments, and the following/follower relationships and timestamps. After data preprocessing, the daily data is segmented into weekly intervals (64 times points), where each time step t represents the end of a week. The final dataset contains 143 projects and 300 users. We further extract the following information as input features:

* User weekly comment/post counts contain new generated comment/post counts for each user.

* User weekly RFE scores contain weekly RFE scores calculated by the method introduced in Section 3.3. The scores represent the user's engagement level, with higher scores reflecting more active and engaged users.

* User knowledge sharing contents include weekly new generated comment/post contents at the individual user level. For each week, we combine the content of the comments and posts made by a user into one paragraph.

<footnote>$^2$https://jogl.io/</footnote>
* Following/follower network is a dynamic user interaction network built based on users' follower and following behavior. For each week, we build a network based on following and follower time stamps. There are totally 64 networks.

* User Engagement Level Class Labels consist of two categories: active and inactive. At each time step, a user is classified as either active or inactive based on their RFE values, as determined by Equation 2.

## 4.2 Metrics

The metrics used to evaluate the classification prediction performance are Accuracy and F1-score. In our framework, the direct predicted values $\hat{o}_i$ from KEGNN model are real values. After training and predicting, we use a threshold $\tau$ to classify both ground truth RFE values $o_i$ and predicted RFE values $\hat{o}_i$ into 'active' or 'inactive' user categories based on Equation 2. A prediction is considered correct if categorical predicted $\hat{y}_i$ correctly identifies the user's engagement level $y_i$ (active or inactive), i.e., $\hat{y}_i = y_i$.

## 4.3 Baselines

To serve as baselines, we implemented several classic time series and graph-based forecasting models and advanced spatial-temporal forecasting models. It is important to note that these models were not originally designed to address our specific problem. We adapted them to our context without incorporating textual knowledge from the input features, as these models lack the capability to process text information.

* Autoregressive Moving Average (ARMA) [10] is a traditional time series model. The AR order is set to 2, and the MA order is set to 5. The input includes only historical time series RFE values.

* RNN is a vanilla RNN with one hidden layer and a look-back window of 3, using historical RFE and weekly counts as input.

* GNN uses two GCN layers to model dynamic graphs based on following/follower relationships, with node features as historical RFE and weekly counts.

* LSTNet [25] is a neural network architecture that combines convolutional layers, recurrent layers, and an autoregressive component.

* ColaGNN [12] is a GNN-based model designed to predict seasonal flu case counts by combining a static network structure with an attention mechanism. We adapted this framework to suit our problem formulation, where the static network represents the following/follower relationships.

## 4.4 Hyper-parameter Setting & Implementation Details

In our experiments, we evaluate model performance at different predicting horizons $h = [1, 2, 3]$ where a time unit is a week. The default setting of the proposed model is: $T=3$, $D_1=D_2=40$, $B=300$, $C=64$, $L_{rnn}=L_{gnn}=1$, $F^1=F^2=40$. All models are trained for 1500 epochs with a batch size of 8, implementing early stopping based on validation loss. Each experiment runs for 5 random trials and results are averaged.

For the testing dataset, we use data from the last four weeks, spanning June 14, 2021 to July 12, 2021. The remaining instances are split into training and validation datasets using a 9:1 ratio. In RFE model, we set $a = 0.25$, $b = 0.25$, and $c = 0.5$ in Equation 3. We set $\tau = 2$ in Equation 2. In sensitivity analysis, we explore hidden state dimension $D_1 = [20, 40^*, 80]$, number of RNN layers $L_{rnn} = [1^*, 2]$, number of convolution layers $L_{gnn} = [1^*, 2]$, and historical window sizes $T = [1, 3^*, 5]$, where * indicates the default values. The scaling distributions for R, F, and E are: R scales on 0-30-60-90-120-*; F scales on 0-1-2-3-4-*; E scales on 0-1-2-4-6-*.

# 5 Result

## 5.1 Model Predicting Performance

We evaluate our framework and baselines on predicting user engagement levels with horizons of 1, 2, and 3 week ahead for target testing weeks. Table 2 shows the predicting performance in terms of Accuracy and F1-score.

Overall, our proposed method, KEGNN, consistently outperforms the baseline models across all horizons in both accuracy and F1-score. For short-term predictions (Horizon = 1), KEGNN achieves the highest accuracy (0.8302) and F1-score (0.8915), surpassing ColaGNN, the next-best model, by a significant margin. This suggests that the attention mechanism may not provide significant benefits in a highly dynamic and complex network learning context. Furthermore, integrating knowledge sharing content appears to be a key factor in achieving better performance for predicting user's future behavior in crowd-empowered online communities. We explore these insights further in the ablation study presented in Section 5.2.

As the prediction horizon increases, the performance of all models declines due to the increased difficulty of long-term forecasting (three weeks). We note that long-term forecasting for online user behavior is much more difficult due to the inherent unpredictability and volatility of user actions over extended periods, as well as the challenge of capturing the evolving dynamics of online interactions and engagement patterns. However, KEGNN maintains its superiority, achieving 0.5683 accuracy and 0.5666 F1-score at Horizon = 2 and 0.3509 accuracy and 0.3469 F1-score at Horizon = 3, consistently surpassing other advanced spatial temporal models such as ColaGNN and LSTNet. These results highlight KEGNN's robustness and its ability to effectively leverage graph-based features, user behavior knowledge, and textual information for accurate predictions, even under challenging conditions.

The traditional statistical time series model ARMA, along with simpler deep learning models like RNN and GNN, demonstrates inferior performance compared to advanced spatial-temporal models. They fail to capture the complex and dynamic nature of user behaviors in our data. This implies that more sophisticated models, which can effectively model spatial and temporal dependencies, are essential for accurately predicting user engagement and behavior over time.

## 5.2 Ablation Study

To investigate the effect of each module in our framework, we conduct an ablation analysis using the default setting of KEGNN.
<table><caption>Table 2: Overall experimental results of model performance on the testing set with different horizons (1, 2, 3). Bold face indicates the best results in each column. The results are an average of 5 random runs.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">Horizon = 1</th><th colspan="2">Horizon = 2</th><th colspan="2">Horizon = 3</th></tr><tr><th>Accuracy (↑)</th><th>F1-score (↑)</th><th>Accuracy (↑)</th><th>F1-score (↑)</th><th>Accuracy (↑)</th><th>F1-score (↑)</th></tr></thead><tbody><tr><td>ARMA</td><td>0.3849</td><td>0.6388</td><td>0.2445</td><td>0.1948</td><td>0.1056</td><td>0.1278</td></tr><tr><td>RNN</td><td>0.5950</td><td>0.6218</td><td>0.3294</td><td>0.3124</td><td>0.1379</td><td>0.2260</td></tr><tr><td>GNN</td><td>0.6095</td><td>0.6218</td><td>0.3200</td><td>0.3172</td><td>0.1433</td><td>0.1300</td></tr><tr><td>LSTNet</td><td>0.7661</td><td>0.7809</td><td>0.4271</td><td>0.4206</td><td>0.2759</td><td>0.2880</td></tr><tr><td>ColaGNN</td><td>0.8150</td><td>0.8218</td><td>0.4989</td><td>0.5073</td><td>0.3299</td><td>0.3135</td></tr><tr><td>KEGNN</td><td><strong>0.8302</strong></td><td><strong>0.8915</strong></td><td><strong>0.5683</strong></td><td><strong>0.5666</strong></td><td><strong>0.3509</strong></td><td><strong>0.3469</strong></td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>Figure 2: Sensitivity analysis of hyperparameters. Each result is obtained using the default model settings, with the exception of the hyperparameter being varied.</figcaption></figure>

<table><caption>Table 3: The ablation analysis results. Boldface indicates the best performance in each column. All results are averaged over 5 random runs.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">Accuracy(↑)</th></tr><tr><th>Horizon=1</th><th>Horizon=2</th><th>Horizon=3</th></tr></thead><tbody><tr><td>KEGNN</td><td><strong>0.8302</strong></td><td><strong>0.5683</strong></td><td><strong>0.3509</strong></td></tr><tr><td>KEGNN w/o NLP</td><td>0.7823</td><td>0.4287</td><td>0.1905</td></tr><tr><td>KEGNN w/o RFE</td><td>0.4013</td><td>0.2706</td><td>0.1121</td></tr><tr><td>KEGNN only RFE</td><td>0.6095</td><td>0.3200</td><td>0.1433</td></tr><tr><td>KEGNN attention</td><td>0.8179</td><td>0.5591</td><td>0.3329</td></tr></tbody></table>

* KEGNN w/o NLP: Remove the NLP module from the proposed model, and use the historical time series of RFE and comment/post count as the input features.

* KEGNN w/o RFE: Remove the historical RFE time series from the input features, and use comment/post count time series and knowledge contents as the input features.

* KEGNN only RFE: Remove the NLP module and comment/post count time series features, only use historical RFE time series as the input features.

* KEGNN attention: replaces the dynamic following/follower network with a learned attention coefficient matrix.

Table 3 presents the numerical Accuracy results for the ablation analysis. The results show that KEGNN consistently outperforms the other variations of the model across all horizons. KEGNN w/o

RFE exhibits a sharp decline in performance, emphasizing the importance of historical RFE time series in predicting user future RFE scores. Removing the NLP component (KEGNN w/o NLP) leads to a noticeable decrease in accuracy at all horizons, falling below ColaGNN, highlighting the importance of knowledge integration for capturing complex user engagement patterns. Our framework leverages this effectively for better predictions. When only RFE is used (KEGNN only RFE), the model still performs poorly, reinforcing that complementary features of user behavior are essential for improving model performance in predicting future behavior. KEGNN attention performs better than other variants but underperforms compared to the full model, possibly due to its limited ability to capture sparse and dynamic user interactions. Hybrid models combining attention and dynamic networks is a potential direction for future work. Lastly, KEGNN attention shows superior performance compared to the other variation models but still lags behind the full KEGNN model, implying that while the attention mechanism can be a good alternative in the absence of real-time dynamic networks, its performance does not match that of the dynamic networks derived from real-world data. Overall, these findings highlight the importance of integrating knowledge content, complementary features, and real-time dynamic networks to accurately predict user behavior at future time points in online knowledge-sharing communities.

## 5.3 Sensitivity Analysis

We investigate how the model performance varies with the number of RNN layers $L_{rnn} = [1, 2]$, historical window size $T = [1, 3, 5]$, RNN hidden size $D_1 = [20, 40, 80]$, the number of convolution layers
$L_{gnn}$ = [1, 2], and different regularization L1 and L2. The experiments are conducted on testing points with horizon 1. As shown in Figure 2, the results suggest that varying the hyperparameters leads to notable differences in performance, as measured by accuracy and F1-score. Increasing the depth of RNN and convolutional layers reduces performance, indicating that a shallower model better suits our problem due to its reduced complexity and lower risk of overfitting. Similarly, an appropriate hidden size balances accuracy and complexity. For historical window size, we find that performance initially improves as the window size increases but eventually declines. This pattern suggests that a short window may fail to capture sufficient temporal information, while an excessively long window may dilute the significance of recent values, which are more relevant to prediction. L2 regularization outperforms L1, as its compatibility with $\ell_2$-norm loss function ensures smoother weight updates and better generalization.

## 6 Conclusion

In this work, we address the challenge of predicting user engagement in crowd-empowered online communities by proposing the KEGNN framework. Our framework begins with collecting and refining user activity data from a real-world platform, JOGL, and introduces the RFE model to calculate temporal RFE scores that quantify user engagement from complex activity records. KEGNN is designed to capture both temporal and spatial patterns in user behavior dynamics, while also incorporating user-generated textual knowledge into graph learning, significantly enhancing predictive performance. It aligns with real-world scenarios where member engagement is key to platform success. By encouraging knowledge sharing, developers can boost member activeness and enhance engagement. Experimental results demonstrate that KEGNN outperforms baseline models in short- and long-term predictive horizons, highlighting its effectiveness. This framework not only serves as a robust foundation for analyzing online user behaviors but also has the potential to be extended to other domains, such as fraud detection, healthcare, and recommendation systems, offering valuable insights into complex behavioral dynamics. Future work will focus on optimizing the model's efficiency for large-scale deployment. In addition, we plan to expand the evaluations to additional datasets to further validate its effectiveness.

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