70 research outputs found
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
We study the problem of clustering nodes in a dynamic graph, where the
connections between nodes and nodes' cluster memberships may change over time,
e.g., due to community migration. We first propose a dynamic stochastic block
model that captures these changes, and a simple decay-based clustering
algorithm that clusters nodes based on weighted connections between them, where
the weight decreases at a fixed rate over time. This decay rate can then be
interpreted as signifying the importance of including historical connection
information in the clustering. However, the optimal decay rate may differ for
clusters with different rates of turnover. We characterize the optimal decay
rate for each cluster and propose a clustering method that achieves almost
exact recovery of the true clusters. We then demonstrate the efficacy of our
clustering algorithm with optimized decay rates on simulated graph data.
Recurrent neural networks (RNNs), a popular algorithm for sequence learning,
use a similar decay-based method, and we use this insight to propose two new
RNN-GCN (graph convolutional network) architectures for semi-supervised graph
clustering. We finally demonstrate that the proposed architectures perform well
on real data compared to state-of-the-art graph clustering algorithms
Observe Before Play: Multi-armed Bandit with Pre-observations
We consider the stochastic multi-armed bandit (MAB) problem in a setting
where a player can pay to pre-observe arm rewards before playing an arm in each
round. Apart from the usual trade-off between exploring new arms to find the
best one and exploiting the arm believed to offer the highest reward, we
encounter an additional dilemma: pre-observing more arms gives a higher chance
to play the best one, but incurs a larger cost. For the single-player setting,
we design an Observe-Before-Play Upper Confidence Bound (OBP-UCB) algorithm for
arms with Bernoulli rewards, and prove a -round regret upper bound
. In the multi-player setting, collisions will occur when players
select the same arm to play in the same round. We design a centralized
algorithm, C-MP-OBP, and prove its -round regret relative to an offline
greedy strategy is upper bounded in for arms and
players. We also propose distributed versions of the C-MP-OBP policy,
called D-MP-OBP and D-MP-Adapt-OBP, achieving logarithmic regret with respect
to collision-free target policies. Experiments on synthetic data and wireless
channel traces show that C-MP-OBP and D-MP-OBP outperform random heuristics and
offline optimal policies that do not allow pre-observations
Proportional Fair RAT Aggregation in HetNets
Heterogeneity in wireless network architectures (i.e., the coexistence of 3G,
LTE, 5G, WiFi, etc.) has become a key component of current and future
generation cellular networks. Simultaneous aggregation of each client's traffic
across multiple such radio access technologies (RATs) / base stations (BSs) can
significantly increase the system throughput, and has become an important
feature of cellular standards on multi-RAT integration. Distributed algorithms
that can realize the full potential of this aggregation are thus of great
importance to operators. In this paper, we study the problem of resource
allocation for multi-RAT traffic aggregation in HetNets (heterogeneous
networks). Our goal is to ensure that the resources at each BS are allocated so
that the aggregate throughput achieved by each client across its RATs satisfies
a proportional fairness (PF) criterion. In particular, we provide a simple
distributed algorithm for resource allocation at each BS that extends the PF
allocation algorithm for a single BS. Despite its simplicity and lack of
coordination across the BSs, we show that our algorithm converges to the
desired PF solution and provide (tight) bounds on its convergence speed. We
also study the characteristics of the optimal solution and use its properties
to prove the optimality of our algorithm's outcomes.Comment: Extended version of the 31st International Teletraffic Congress (ITC
2019) conference pape
Intelligent Communication Planning for Constrained Environmental IoT Sensing with Reinforcement Learning
Internet of Things (IoT) technologies have enabled numerous data-driven
mobile applications and have the potential to significantly improve
environmental monitoring and hazard warnings through the deployment of a
network of IoT sensors. However, these IoT devices are often power-constrained
and utilize wireless communication schemes with limited bandwidth. Such power
constraints limit the amount of information each device can share across the
network, while bandwidth limitations hinder sensors' coordination of their
transmissions. In this work, we formulate the communication planning problem of
IoT sensors that track the state of the environment. We seek to optimize
sensors' decisions in collecting environmental data under stringent resource
constraints. We propose a multi-agent reinforcement learning (MARL) method to
find the optimal communication policies for each sensor that maximize the
tracking accuracy subject to the power and bandwidth limitations. MARL learns
and exploits the spatial-temporal correlation of the environmental data at each
sensor's location to reduce the redundant reports from the sensors. Experiments
on wildfire spread with LoRA wireless network simulators show that our MARL
method can learn to balance the need to collect enough data to predict wildfire
spread with unknown bandwidth limitations.Comment: To be published in the 20th Annual IEEE International Conference on
Sensing, Communication, and Networking (SECON 2023
Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks
With the rapid acceleration of transportation electrification, public
charging stations are becoming vital infrastructure in a smart sustainable city
to provide on-demand electric vehicle (EV) charging services. As more consumers
seek to utilize public charging services, the pricing and scheduling of such
services will become vital, complementary tools to mediate competition for
charging resources. However, determining the right prices to charge is
difficult due to the online nature of EV arrivals. This paper studies a joint
pricing and scheduling problem for the operator of EV charging networks with
limited charging capacity and time-varying energy cost. Upon receiving a
charging request, the operator offers a price, and the EV decides whether to
admit the offer based on its own value and the posted price. The operator then
schedules the real-time charging process to satisfy the charging request if the
EV admits the offer. We propose an online pricing algorithm that can determine
the posted price and EV charging schedule to maximize social welfare, i.e., the
total value of EVs minus the energy cost of charging stations. Theoretically,
we prove the devised algorithm can achieve the order-optimal competitive ratio
under the competitive analysis framework. Practically, we show the empirical
performance of our algorithm outperforms other benchmark algorithms in
experiments using real EV charging data
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