33 research outputs found
Answering Top-k Queries Over a Mixture of Attractive and Repulsive Dimensions
In this paper, we formulate a top-k query that compares objects in a database
to a user-provided query object on a novel scoring function. The proposed
scoring function combines the idea of attractive and repulsive dimensions into
a general framework to overcome the weakness of traditional distance or
similarity measures. We study the properties of the proposed class of scoring
functions and develop efficient and scalable index structures that index the
isolines of the function. We demonstrate various scenarios where the query
finds application. Empirical evaluation demonstrates a performance gain of one
to two orders of magnitude on querying time over existing state-of-the-art
top-k techniques. Further, a qualitative analysis is performed on a real
dataset to highlight the potential of the proposed query in discovering hidden
data characteristics.Comment: VLDB201
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Graph neural networks (GNNs), in general, are built on the assumption of a
static set of features characterizing each node in a graph. This assumption is
often violated in practice. Existing methods partly address this issue through
feature imputation. However, these techniques (i) assume uniformity of feature
set across nodes, (ii) are transductive by nature, and (iii) fail to work when
features are added or removed over time. In this work, we address these
limitations through a novel GNN framework called GRAFENNE. GRAFENNE performs a
novel allotropic transformation on the original graph, wherein the nodes and
features are decoupled through a bipartite encoding. Through a carefully chosen
message passing framework on the allotropic transformation, we make the model
parameter size independent of the number of features and thereby inductive to
both unseen nodes and features. We prove that GRAFENNE is at least as
expressive as any of the existing message-passing GNNs in terms of
Weisfeiler-Leman tests, and therefore, the additional inductivity to unseen
features does not come at the cost of expressivity. In addition, as
demonstrated over four real-world graphs, GRAFENNE empowers the underlying GNN
with high empirical efficacy and the ability to learn in continual fashion over
streaming feature sets.Comment: 17 pages, 4 figures and 9 tables. Accepted in ICML 2023, DOI will be
updated once it is availabl
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Deep graph generative modeling has gained enormous attraction in recent years
due to its impressive ability to directly learn the underlying hidden graph
distribution. Despite their initial success, these techniques, like much of the
existing deep generative methods, require a large number of training samples to
learn a good model. Unfortunately, large number of training samples may not
always be available in scenarios such as drug discovery for rare diseases. At
the same time, recent advances in few-shot learning have opened door to
applications where available training data is limited. In this work, we
introduce the hitherto unexplored paradigm of few-shot graph generative
modeling. Towards this, we develop GSHOT, a meta-learning based framework for
few-shot labeled graph generative modeling. GSHOT learns to transfer
meta-knowledge from similar auxiliary graph datasets. Utilizing these prior
experiences, GSHOT quickly adapts to an unseen graph dataset through self-paced
fine-tuning. Through extensive experiments on datasets from diverse domains
having limited training samples, we establish that GSHOT generates graphs of
superior fidelity compared to existing baselines
Trajectory Aware Macro-cell Planning for Mobile Users
We design and evaluate algorithms for efficient user-mobility driven
macro-cell planning in cellular networks. As cellular networks embrace
heterogeneous technologies (including long range 3G/4G and short range WiFi,
Femto-cells, etc.), most traffic generated by static users gets absorbed by the
short-range technologies, thereby increasingly leaving mobile user traffic to
macro-cells. To this end, we consider a novel approach that factors in the
trajectories of mobile users as well as the impact of city geographies and
their associated road networks for macro-cell planning. Given a budget k of
base-stations that can be upgraded, our approach selects a deployment that
impacts the most number of user trajectories. The generic formulation
incorporates the notion of quality of service of a user trajectory as a
parameter to allow different application-specific requirements, and operator
choices.We show that the proposed trajectory utility maximization problem is
NP-hard, and design multiple heuristics. We evaluate our algorithms with real
and synthetic data sets emulating different city geographies to demonstrate
their efficacy. For instance, with an upgrade budget k of 20%, our algorithms
perform 3-8 times better in improving the user quality of service on
trajectories in different city geographies when compared to greedy
location-based base-station upgrades.Comment: Published in INFOCOM 201
NeuroCUT: A Neural Approach for Robust Graph Partitioning
Graph partitioning aims to divide a graph into disjoint subsets while
optimizing a specific partitioning objective. The majority of formulations
related to graph partitioning exhibit NP-hardness due to their combinatorial
nature. As a result, conventional approximation algorithms rely on heuristic
methods, sometimes with approximation guarantees and sometimes without.
Unfortunately, traditional approaches are tailored for specific partitioning
objectives and do not generalize well across other known partitioning
objectives from the literature. To overcome this limitation, and learn
heuristics from the data directly, neural approaches have emerged,
demonstrating promising outcomes. In this study, we extend this line of work
through a novel framework, NeuroCut. NeuroCut introduces two key innovations
over prevailing methodologies. First, it is inductive to both graph topology
and the partition count, which is provided at query time. Second, by leveraging
a reinforcement learning based framework over node representations derived from
a graph neural network, NeuroCut can accommodate any optimization objective,
even those encompassing non-differentiable functions. Through empirical
evaluation, we demonstrate that NeuroCut excels in identifying high-quality
partitions, showcases strong generalization across a wide spectrum of
partitioning objectives, and exhibits resilience to topological modifications
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
The time evolution of physical systems is described by differential
equations, which depend on abstract quantities like energy and force.
Traditionally, these quantities are derived as functionals based on observables
such as positions and velocities. Discovering these governing symbolic laws is
the key to comprehending the interactions in nature. Here, we present a
Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the
dynamics of systems directly from their trajectory. We demonstrate the
performance of HGNN on n-springs, n-pendulums, gravitational systems, and
binary Lennard Jones systems; HGNN learns the dynamics in excellent agreement
with the ground truth from small amounts of data. We also evaluate the ability
of HGNN to generalize to larger system sizes, and to hybrid spring-pendulum
system that is a combination of two original systems (spring and pendulum) on
which the models are trained independently. Finally, employing symbolic
regression on the learned HGNN, we infer the underlying equations relating the
energy functionals, even for complex systems such as the binary Lennard-Jones
liquid. Our framework facilitates the interpretable discovery of interaction
laws directly from physical system trajectories. Furthermore, this approach can
be extended to other systems with topology-dependent dynamics, such as cells,
polydisperse gels, or deformable bodies