994 research outputs found
A Causal Disentangled Multi-Granularity Graph Classification Method
Graph data widely exists in real life, with large amounts of data and complex
structures. It is necessary to map graph data to low-dimensional embedding.
Graph classification, a critical graph task, mainly relies on identifying the
important substructures within the graph. At present, some graph classification
methods do not combine the multi-granularity characteristics of graph data.
This lack of granularity distinction in modeling leads to a conflation of key
information and false correlations within the model. So, achieving the desired
goal of a credible and interpretable model becomes challenging. This paper
proposes a causal disentangled multi-granularity graph representation learning
method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the
important substructures and bias parts within the graph from a
multi-granularity perspective. The disentanglement of the CDM-GNN model reveals
important and bias parts, forming the foundation for its classification task,
specifically, model interpretations. The CDM-GNN model exhibits strong
classification performance and generates explanatory outcomes aligning with
human cognitive patterns. In order to verify the effectiveness of the model,
this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six
state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are
employed for comparison purposes. Additionally, a qualitative analysis of the
interpretation results is conducted
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions
Signed graphs are complex systems that represent trust relationships or
preferences in various domains. Learning node representations in such graphs is
crucial for many mining tasks. Although real-world signed relationships can be
influenced by multiple latent factors, most existing methods often oversimplify
the modeling of signed relationships by relying on social theories and treating
them as simplistic factors. This limits their expressiveness and their ability
to capture the diverse factors that shape these relationships. In this paper,
we propose DINES, a novel method for learning disentangled node representations
in signed directed graphs without social assumptions. We adopt a disentangled
framework that separates each embedding into distinct factors, allowing for
capturing multiple latent factors. We also explore lightweight graph
convolutions that focus solely on sign and direction, without depending on
social theories. Additionally, we propose a decoder that effectively classifies
an edge's sign by considering correlations between the factors. To further
enhance disentanglement, we jointly train a self-supervised factor
discriminator with our encoder and decoder. Throughout extensive experiments on
real-world signed directed graphs, we show that DINES effectively learns
disentangled node representations, and significantly outperforms its
competitors in the sign prediction task.Comment: 26 pages, 11 figure
Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks
Graph neural networks (GNNs) have become compelling models designed to
perform learning and inference on graph-structured data. However, little work
has been done to understand the fundamental limitations of GNNs for scaling to
larger graphs and generalizing to out-of-distribution (OOD) inputs. In this
paper, we use a random graph generator to systematically investigate how the
graph size and structural properties affect the predictive performance of GNNs.
We present specific evidence that the average node degree is a key feature in
determining whether GNNs can generalize to unseen graphs, and that the use of
multiple node update functions can improve the generalization performance of
GNNs when dealing with graphs of multimodal degree distributions. Accordingly,
we propose a multi-module GNN framework that allows the network to adapt
flexibly to new graphs by generalizing a single canonical nonlinear
transformation over aggregated inputs. Our results show that the multi-module
GNNs improve the OOD generalization on a variety of inference tasks in the
direction of diverse structural features
Controllable Recommenders using Deep Generative Models and Disentanglement
In this paper, we consider controllability as a means to satisfy dynamic
preferences of users, enabling them to control recommendations such that their
current preference is met. While deep models have shown improved performance
for collaborative filtering, they are generally not amenable to fine grained
control by a user, leading to the development of methods like deep language
critiquing. We propose an alternate view, where instead of keyphrase based
critiques, a user is provided 'knobs' in a disentangled latent space, with each
knob corresponding to an item aspect. Disentanglement here refers to a latent
space where generative factors (here, a preference towards an item category
like genre) are captured independently in their respective dimensions, thereby
enabling predictable manipulations, otherwise not possible in an entangled
space. We propose using a (semi-)supervised disentanglement objective for this
purpose, as well as multiple metrics to evaluate the controllability and the
degree of personalization of controlled recommendations. We show that by
updating the disentangled latent space based on user feedback, and by
exploiting the generative nature of the recommender, controlled and
personalized recommendations can be produced. Through experiments on two widely
used collaborative filtering datasets, we demonstrate that a controllable
recommender can be trained with a slight reduction in recommender performance,
provided enough supervision is provided. The recommendations produced by these
models appear to both conform to a user's current preference and remain
personalized.Comment: 10 pages, 1 figur
Regulatory Independence and Political Interference: Evidence from EU Mixed-Ownership Utilities’ Investment and Debt
This paper examines the investment and financial decisions of a sample of 92 EU regulated utilities, taking into account key institutional features of EU public utilities, such as: a) regulation by agencies with various degrees of independence; b) partial ownership of the state in the regulated firm; and c) the government’s political orientation, which may ultimately influence the regulatory climate to be either more pro-firm or more pro-consumers. Our results show that regulatory independence matters for both investment and financial decisions. Investment increases under an Independent Regulatory Agency (IRA), while ownership has no effect. Leverage also increases when the IRA is in place, especially so if the regulated firm is privately controlled. Finally political orientation does matter, as firm investment increases under more conservative (pro-firm) governments, but this effect appears to revert when the IRA is in place.Regulated Utilities, Investment, Capital Structure, Private and State Ownership, Regulatory Independence, overnment’s Political Orientation
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