998 research outputs found
Energy flow polynomials: A complete linear basis for jet substructure
We introduce the energy flow polynomials: a complete set of jet substructure
observables which form a discrete linear basis for all infrared- and
collinear-safe observables. Energy flow polynomials are multiparticle energy
correlators with specific angular structures that are a direct consequence of
infrared and collinear safety. We establish a powerful graph-theoretic
representation of the energy flow polynomials which allows us to design
efficient algorithms for their computation. Many common jet observables are
exact linear combinations of energy flow polynomials, and we demonstrate the
linear spanning nature of the energy flow basis by performing regression for
several common jet observables. Using linear classification with energy flow
polynomials, we achieve excellent performance on three representative jet
tagging problems: quark/gluon discrimination, boosted W tagging, and boosted
top tagging. The energy flow basis provides a systematic framework for complete
investigations of jet substructure using linear methods.Comment: 41+15 pages, 13 figures, 5 tables; v2: updated to match JHEP versio
Learning Hierarchical Review Graph Representations for Recommendation
The user review data have been demonstrated to be effective in solving
different recommendation problems. Previous review-based recommendation methods
usually employ sophisticated compositional models, such as Recurrent Neural
Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic
representations from the review data for recommendation. However, these methods
mainly capture the local dependency between neighbouring words in a word
window, and they treat each review equally. Therefore, they may not be
effective in capturing the global dependency between words, and tend to be
easily biased by noise review information. In this paper, we propose a novel
review-based recommendation model, named Review Graph Neural Network (RGNN).
Specifically, RGNN builds a specific review graph for each individual
user/item, which provides a global view about the user/item properties to help
weaken the biases caused by noise review information. A type-aware graph
attention mechanism is developed to learn semantic embeddings of words.
Moreover, a personalized graph pooling operator is proposed to learn
hierarchical representations of the review graph to form the semantic
representation for each user/item. We compared RGNN with state-of-the-art
review-based recommendation approaches on two real-world datasets. The
experimental results indicate that RGNN consistently outperforms baseline
methods, in terms of Mean Square Error (MSE)
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs
Subgraphs are rich substructures in graphs, and their nodes and edges can be
partially observed in real-world tasks. Under partial observation, existing
node- or subgraph-level message-passing produces suboptimal representations. In
this paper, we formulate a novel task of learning representations of partially
observed subgraphs. To solve this problem, we propose Partial Subgraph InfoMax
(PSI) framework and generalize existing InfoMax models, including DGI,
InfoGraph, MVGRL, and GraphCL, into our framework. These models maximize the
mutual information between the partial subgraph's summary and various
substructures from nodes to full subgraphs. In addition, we suggest a novel
two-stage model with -hop PSI, which reconstructs the representation of the
full subgraph and improves its expressiveness from different local-global
structures. Under training and evaluation protocols designed for this problem,
we conduct experiments on three real-world datasets and demonstrate that PSI
models outperform baselines.Comment: CIKM 2022 Short Paper (Camera-ready + Appendix
Kernel-based Substructure Exploration for Next POI Recommendation
Point-of-Interest (POI) recommendation, which benefits from the proliferation
of GPS-enabled devices and location-based social networks (LBSNs), plays an
increasingly important role in recommender systems. It aims to provide users
with the convenience to discover their interested places to visit based on
previous visits and current status. Most existing methods usually merely
leverage recurrent neural networks (RNNs) to explore sequential influences for
recommendation. Despite the effectiveness, these methods not only neglect
topological geographical influences among POIs, but also fail to model
high-order sequential substructures. To tackle the above issues, we propose a
Kernel-Based Graph Neural Network (KBGNN) for next POI recommendation, which
combines the characteristics of both geographical and sequential influences in
a collaborative way. KBGNN consists of a geographical module and a sequential
module. On the one hand, we construct a geographical graph and leverage a
message passing neural network to capture the topological geographical
influences. On the other hand, we explore high-order sequential substructures
in the user-aware sequential graph using a graph kernel neural network to
capture user preferences. Finally, a consistency learning framework is
introduced to jointly incorporate geographical and sequential information
extracted from two separate graphs. In this way, the two modules effectively
exchange knowledge to mutually enhance each other. Extensive experiments
conducted on two real-world LBSN datasets demonstrate the superior performance
of our proposed method over the state-of-the-arts. Our codes are available at
https://github.com/Fang6ang/KBGNN.Comment: Accepted by the IEEE International Conference on Data Mining (ICDM)
202
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Contrastive deep graph clustering, which aims to divide nodes into disjoint
groups via contrastive mechanisms, is a challenging research spot. Among the
recent works, hard sample mining-based algorithms have achieved great attention
for their promising performance. However, we find that the existing hard sample
mining methods have two problems as follows. 1) In the hardness measurement,
the important structural information is overlooked for similarity calculation,
degrading the representativeness of the selected hard negative samples. 2)
Previous works merely focus on the hard negative sample pairs while neglecting
the hard positive sample pairs. Nevertheless, samples within the same cluster
but with low similarity should also be carefully learned. To solve the
problems, we propose a novel contrastive deep graph clustering method dubbed
Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity
measure criterion and a general dynamic sample weighing strategy. Concretely,
in our algorithm, the similarities between samples are calculated by
considering both the attribute embeddings and the structure embeddings, better
revealing sample relationships and assisting hardness measurement. Moreover,
under the guidance of the carefully collected high-confidence clustering
information, our proposed weight modulating function will first recognize the
positive and negative samples and then dynamically up-weight the hard sample
pairs while down-weighting the easy ones. In this way, our method can mine not
only the hard negative samples but also the hard positive sample, thus
improving the discriminative capability of the samples further. Extensive
experiments and analyses demonstrate the superiority and effectiveness of our
proposed method.Comment: 9 pages, 6 figure
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
Reinforced Imitative Graph Learning for Mobile User Profiling
Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach
- …