275 research outputs found
COIN: Co-Cluster Infomax for Bipartite Graphs
Bipartite graphs are powerful data structures to model interactions between
two types of nodes, which have been used in a variety of applications, such as
recommender systems, information retrieval, and drug discovery. A fundamental
challenge for bipartite graphs is how to learn informative node embeddings.
Despite the success of recent self-supervised learning methods on bipartite
graphs, their objectives are discriminating instance-wise positive and negative
node pairs, which could contain cluster-level errors. In this paper, we
introduce a novel co-cluster infomax (COIN) framework, which captures the
cluster-level information by maximizing the mutual information of co-clusters.
Different from previous infomax methods which estimate mutual information by
neural networks, COIN could easily calculate mutual information. Besides, COIN
is an end-to-end coclustering method which can be trained jointly with other
objective functions and optimized via back-propagation. Furthermore, we also
provide theoretical analysis for COIN. We theoretically prove that COIN is able
to effectively increase the mutual information of node embeddings and COIN is
upper-bounded by the prior distributions of nodes. We extensively evaluate the
proposed COIN framework on various benchmark datasets and tasks to demonstrate
the effectiveness of COIN.Comment: NeurIPS 2022 GLFrontiers Worksho
FairGen: Towards Fair Graph Generation
There have been tremendous efforts over the past decades dedicated to the
generation of realistic graphs in a variety of domains, ranging from social
networks to computer networks, from gene regulatory networks to online
transaction networks. Despite the remarkable success, the vast majority of
these works are unsupervised in nature and are typically trained to minimize
the expected graph reconstruction loss, which would result in the
representation disparity issue in the generated graphs, i.e., the protected
groups (often minorities) contribute less to the objective and thus suffer from
systematically higher errors. In this paper, we aim to tailor graph generation
to downstream mining tasks by leveraging label information and user-preferred
parity constraint. In particular, we start from the investigation of
representation disparity in the context of graph generative models. To mitigate
the disparity, we propose a fairness-aware graph generative model named
FairGen. Our model jointly trains a label-informed graph generation module and
a fair representation learning module by progressively learning the behaviors
of the protected and unprotected groups, from the `easy' concepts to the `hard'
ones. In addition, we propose a generic context sampling strategy for graph
generative models, which is proven to be capable of fairly capturing the
contextual information of each group with a high probability. Experimental
results on seven real-world data sets, including web-based graphs, demonstrate
that FairGen (1) obtains performance on par with state-of-the-art graph
generative models across six network properties, (2) mitigates the
representation disparity issues in the generated graphs, and (3) substantially
boosts the model performance by up to 17% in downstream tasks via data
augmentation
Adversarial Attacks on Fairness of Graph Neural Networks
Fairness-aware graph neural networks (GNNs) have gained a surge of attention
as they can reduce the bias of predictions on any demographic group (e.g.,
female) in graph-based applications. Although these methods greatly improve the
algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully
designed adversarial attacks. In this paper, we investigate the problem of
adversarial attacks on fairness of GNNs and propose G-FairAttack, a general
framework for attacking various types of fairness-aware GNNs in terms of
fairness with an unnoticeable effect on prediction utility. In addition, we
propose a fast computation technique to reduce the time complexity of
G-FairAttack. The experimental study demonstrates that G-FairAttack
successfully corrupts the fairness of different types of GNNs while keeping the
attack unnoticeable. Our study on fairness attacks sheds light on potential
vulnerabilities in fairness-aware GNNs and guides further research on the
robustness of GNNs in terms of fairness. The open-source code is available at
https://github.com/zhangbinchi/G-FairAttack.Comment: 32 pages, 5 figure
Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders
Multivariate time series (MTS) imputation is a widely studied problem in
recent years. Existing methods can be divided into two main groups, including
(1) deep recurrent or generative models that primarily focus on time series
features, and (2) graph neural networks (GNNs) based models that utilize the
topological information from the inherent graph structure of MTS as relational
inductive bias for imputation. Nevertheless, these methods either neglect
topological information or assume the graph structure is fixed and accurately
known. Thus, they fail to fully utilize the graph dynamics for precise
imputation in more challenging MTS data such as networked time series (NTS),
where the underlying graph is constantly changing and might have missing edges.
In this paper, we propose a novel approach to overcome these limitations.
First, we define the problem of imputation over NTS which contains missing
values in both node time series features and graph structures. Then, we design
a new model named PoGeVon which leverages variational autoencoder (VAE) to
predict missing values over both node time series features and graph
structures. In particular, we propose a new node position embedding based on
random walk with restart (RWR) in the encoder with provable higher expressive
power compared with message-passing based graph neural networks (GNNs). We
further design a decoder with 3-stage predictions from the perspective of
multi-task learning to impute missing values in both time series and graph
structures reciprocally. Experiment results demonstrate the effectiveness of
our model over baselines.Comment: KDD 202
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Portfolio management (PM) is a fundamental financial planning task that aims
to achieve investment goals such as maximal profits or minimal risks. Its
decision process involves continuous derivation of valuable information from
various data sources and sequential decision optimization, which is a
prospective research direction for reinforcement learning (RL). In this paper,
we propose SARL, a novel State-Augmented RL framework for PM. Our framework
aims to address two unique challenges in financial PM: (1) data heterogeneity
-- the collected information for each asset is usually diverse, noisy and
imbalanced (e.g., news articles); and (2) environment uncertainty -- the
financial market is versatile and non-stationary. To incorporate heterogeneous
data and enhance robustness against environment uncertainty, our SARL augments
the asset information with their price movement prediction as additional
states, where the prediction can be solely based on financial data (e.g., asset
prices) or derived from alternative sources such as news. Experiments on two
real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with
7-year Reuters news articles, validate the effectiveness of SARL over existing
PM approaches, both in terms of accumulated profits and risk-adjusted profits.
Moreover, extensive simulations are conducted to demonstrate the importance of
our proposed state augmentation, providing new insights and boosting
performance significantly over standard RL-based PM method and other baselines.Comment: AAAI 202
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