1,266 research outputs found
DyExplainer: Explainable Dynamic Graph Neural Networks
Graph Neural Networks (GNNs) resurge as a trending research subject owing to
their impressive ability to capture representations from graph-structured data.
However, the black-box nature of GNNs presents a significant challenge in terms
of comprehending and trusting these models, thereby limiting their practical
applications in mission-critical scenarios. Although there has been substantial
progress in the field of explaining GNNs in recent years, the majority of these
studies are centered on static graphs, leaving the explanation of dynamic GNNs
largely unexplored. Dynamic GNNs, with their ever-evolving graph structures,
pose a unique challenge and require additional efforts to effectively capture
temporal dependencies and structural relationships. To address this challenge,
we present DyExplainer, a novel approach to explaining dynamic GNNs on the fly.
DyExplainer trains a dynamic GNN backbone to extract representations of the
graph at each snapshot, while simultaneously exploring structural relationships
and temporal dependencies through a sparse attention technique. To preserve the
desired properties of the explanation, such as structural consistency and
temporal continuity, we augment our approach with contrastive learning
techniques to provide priori-guided regularization. To model longer-term
temporal dependencies, we develop a buffer-based live-updating scheme for
training. The results of our extensive experiments on various datasets
demonstrate the superiority of DyExplainer, not only providing faithful
explainability of the model predictions but also significantly improving the
model prediction accuracy, as evidenced in the link prediction task.Comment: 9 page
A Survey on Explainability of Graph Neural Networks
Graph neural networks (GNNs) are powerful graph-based deep-learning models
that have gained significant attention and demonstrated remarkable performance
in various domains, including natural language processing, drug discovery, and
recommendation systems. However, combining feature information and
combinatorial graph structures has led to complex non-linear GNN models.
Consequently, this has increased the challenges of understanding the workings
of GNNs and the underlying reasons behind their predictions. To address this,
numerous explainability methods have been proposed to shed light on the inner
mechanism of the GNNs. Explainable GNNs improve their security and enhance
trust in their recommendations. This survey aims to provide a comprehensive
overview of the existing explainability techniques for GNNs. We create a novel
taxonomy and hierarchy to categorize these methods based on their objective and
methodology. We also discuss the strengths, limitations, and application
scenarios of each category. Furthermore, we highlight the key evaluation
metrics and datasets commonly used to assess the explainability of GNNs. This
survey aims to assist researchers and practitioners in understanding the
existing landscape of explainability methods, identifying gaps, and fostering
further advancements in interpretable graph-based machine learning.Comment: submitted to Bulletin of the IEEE Computer Society Technical
Committee on Data Engineerin
A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of
knowledge, which underscores the pivotal role of Temporal Knowledge Graphs
(TKGs) in both academia and industry. However, TKGs often suffer from
incompleteness for three main reasons: the continuous emergence of new
knowledge, the weakness of the algorithm for extracting structured information
from unstructured data, and the lack of information in the source dataset.
Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted
increasing attention, aiming to predict missing items based on the available
information. In this paper, we provide a comprehensive review of TKGC methods
and their details. Specifically, this paper mainly consists of three
components, namely, 1)Background, which covers the preliminaries of TKGC
methods, loss functions required for training, as well as the dataset and
evaluation protocol; 2)Interpolation, that estimates and predicts the missing
elements or set of elements through the relevant available information. It
further categorizes related TKGC methods based on how to process temporal
information; 3)Extrapolation, which typically focuses on continuous TKGs and
predicts future events, and then classifies all extrapolation methods based on
the algorithms they utilize. We further pinpoint the challenges and discuss
future research directions of TKGC
EiX-GNN : Concept-level eigencentrality explainer for graph neural networks
Explaining is a human knowledge transfer process regarding a phenomenon
between an explainer and an explainee. Each word used to explain this
phenomenon must be carefully selected by the explainer in accordance with the
current explainee phenomenon-related knowledge level and the phenomenon itself
in order to have a high understanding from the explainee of the phenomenon.
Nowadays, deep models, especially graph neural networks, have a major place in
daily life even in critical applications. In such context, those models need to
have a human high interpretability also referred as being explainable, in order
to improve usage trustability of them in sensitive cases. Explaining is also a
human dependent task and methods that explain deep model behavior must include
these social-related concerns for providing profitable and quality
explanations. Current explaining methods often occlude such social aspect for
providing their explanations and only focus on the signal aspect of the
question. In this contribution we propose a reliable social-aware explaining
method suited for graph neural network that includes this social feature as a
modular concept generator and by both leveraging signal and graph domain aspect
thanks to an eigencentrality concept ordering approach. Besides our method
takes into account the human-dependent aspect underlying any explanation
process, we also reach high score regarding state-of-the-art objective metrics
assessing explanation methods for graph neural networks models
Relational learning on temporal knowledge graphs
Over the last decade, there has been an increasing interest in relational machine learning (RML), which studies methods for the statistical analysis of relational or graph-structured data. Relational data arise naturally in many real-world applications, including social networks, recommender systems, and computational finance. Such data can be represented in the form of a graph consisting of nodes (entities) and labeled edges (relationships between entities). While traditional machine learning techniques are based on feature vectors, RML takes relations into account and permits inference among entities. Recently, performing prediction and learning tasks on knowledge graphs has become a main topic in RML. Knowledge graphs (KGs) are widely used resources for studying multi-relational data in the form of a directed graph, where each labeled edge describes a factual statement, such as (Munich, locatedIn, Germany).
Traditionally, knowledge graphs are considered to represent stationary relationships, which do not change over time. In contrast, event-based multi-relational data exhibits complex temporal dynamics in addition to its multi-relational nature. For example, the political relationship between two countries would intensify because of trade fights; the president of a country may change after an election. To represent the temporal aspect, temporal knowledge graphs (tKGs) were introduced that store a temporal event as a quadruple by extending the static triple with a timestamp describing when this event occurred, i.e. (Barack Obama, visit, India, 2010-11-06). Thus, each edge in the graph has temporal information associated with it and may recur or evolve over time.
Among various learning paradigms on KGs, knowledge representation learning (KRL), also known as knowledge graph embedding, has achieved great success. KRL maps entities and relations into low-dimensional vector spaces while capturing semantic meanings. However, KRL approaches have mostly been done for static KGs and lack the ability to utilize rich temporal dynamics available on tKGs. In this thesis, we study state-of-the-art representation learning techniques for temporal knowledge graphs that can capture temporal dependencies across entities in addition to their relational dependencies. We discover representations for two inference tasks, i.e., tKG forecasting and completion. The former is to forecast future events using historical observations up to the present time, while the latter predicts missing links at observed timestamps. For tKG forecasting, we show how to make the reasoning process interpretable while maintaining performance by employing a sequential reasoning process over local subgraphs. Besides, we propose a continuous-depth multi-relational graph neural network with a novel graph neural ordinary differential equation. It allows for learning continuous-time representations of tKGs, especially in cases with observations in irregular time intervals, as encountered in online analysis. For tKG completion, we systematically review multiple benchmark models. We thoroughly investigate the significance of the proposed temporal encoding technique in each model and provide the first unified open-source framework, which gathers the implementations of well-known tKG completion models. Finally, we discuss the power of geometric learning and show that learning evolving entity representations in a product of Riemannian manifolds can better reflect geometric structures on tKGs and achieve better performances than Euclidean embeddings while requiring significantly fewer model parameters
TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery
Temporal graphs are widely used to model dynamic systems with time-varying
interactions. In real-world scenarios, the underlying mechanisms of generating
future interactions in dynamic systems are typically governed by a set of
recurring substructures within the graph, known as temporal motifs. Despite the
success and prevalence of current temporal graph neural networks (TGNN), it
remains uncertain which temporal motifs are recognized as the significant
indications that trigger a certain prediction from the model, which is a
critical challenge for advancing the explainability and trustworthiness of
current TGNNs. To address this challenge, we propose a novel approach, called
Temporal Motifs Explainer (TempME), which uncovers the most pivotal temporal
motifs guiding the prediction of TGNNs. Derived from the information bottleneck
principle, TempME extracts the most interaction-related motifs while minimizing
the amount of contained information to preserve the sparsity and succinctness
of the explanation. Events in the explanations generated by TempME are verified
to be more spatiotemporally correlated than those of existing approaches,
providing more understandable insights. Extensive experiments validate the
superiority of TempME, with up to 8.21% increase in terms of explanation
accuracy across six real-world datasets and up to 22.96% increase in boosting
the prediction Average Precision of current TGNNs.Comment: Accepted at NeurIPS 2023, Camera Ready Versio
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