Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.

Affinely Adjustable Robust Volt/VAr Control without Centralized Computations

<div>This paper proposes a completely non-centralized Volt/VAr control (VVC) algorithm for active distribution networks which are faced with voltage magnitude violations due to the high penetration of solar photovoltaics (PVs). The proposed VVC algorithm employs a two-stage architecture where the settings of the classical voltage control devices (VCDs) are decided in the first stage through a distributed optimization engine powered by the alternating direction method of multipliers (ADMM). In contrast, the PV smart inverters are instructed in the second stage through linear Q(P) decision rules - which are computed in a decentralized manner by leveraging robust optimization theory. The key to this non-centralized VVC routine is a proposed network partition methodology (NPM) which uses an electrical distance metric based on node Q􀀀jV j2 sensitivitiesfor computing an intermediate reduced graph of the network, which is subsequently divided into the final partitions using the spectral clustering technique. As a result, the final network partitions are connected, stable, close in cardinality, contain at least one PV inverter for zonal reactive power support, and are sufficiently decoupled from each other. Numerical results on the UKGDS-95 bus system show that the non-centralized solutions match closely with the centralized robust VVC schemes, thereby significantly reducing the voltage violations compared to the traditional deterministic VVC routines.</div>

Affinely Adjustable Robust Volt/VAr Control without Centralized Computations

<div>This paper proposes a completely non-centralized Volt/VAr control (VVC) algorithm for active distribution networks which are faced with voltage magnitude violations due to the high penetration of solar photovoltaics (PVs). The proposed VVC algorithm employs a two-stage architecture where the settings of the classical voltage control devices (VCDs) are decided in the first stage through a distributed optimization engine powered by the alternating direction method of multipliers (ADMM). In contrast, the PV smart inverters are instructed in the second stage through linear Q(P) decision rules - which are computed in a decentralized manner by leveraging robust optimization theory. The key to this non-centralized VVC routine is a proposed network partition methodology (NPM) which uses an electrical distance metric based on node Q􀀀jV j2 sensitivitiesfor computing an intermediate reduced graph of the network, which is subsequently divided into the final partitions using the spectral clustering technique. As a result, the final network partitions are connected, stable, close in cardinality, contain at least one PV inverter for zonal reactive power support, and are sufficiently decoupled from each other. Numerical results on the UKGDS-95 bus system show that the non-centralized solutions match closely with the centralized robust VVC schemes, thereby significantly reducing the voltage violations compared to the traditional deterministic VVC routines.</div>

PBeiD Priority-based Information Dissemination Protocol for V2V Communication

In Vehicular Ad-hoc Networks (VANET)s, efficient information dissemination plays a vital role in its successful deployment. Broadcasting has proven as one of the better ways for Information Dissemination over vehicular Networks, and cooperative behaviour among vehicles for information exchange is critical. However, the existing broadcast techniques are still suffering from multiple issues such as Broadcast storm problem, network partition problem, and network contention. Motivated from the aforementioned discussion, in this paper, we propose a Priority-based Efficient Information Dissemination Protocol (PBeiD) to improve the broadcast efficiency in VANETs. PBeiD protocol developed with a blend of probability and density-based information dissemination concepts and implemented in the testbed environment using simulation tools consisting of SUMO, OMNET++, and VEINS. The proposed protocol is compared with benchmark protocols, and the simulation is carried out based on different scenarios from sparse to dense. We found that our protocol is performing well in almost all the cases and to provide proper justification that our results are significant and not by chance, we applied statistical t-test on the results obtained.

Unsupervised Exemplar-Domain Aware Image-to-Image Translation

Image-to-image translation is used to convert an image of a certain style to another of the target style with the original content preserved. A desired translator should be capable of generating diverse results in a controllable many-to-many fashion. To this end, we design a novel deep translator, namely exemplar-domain aware image-to-image translator (EDIT for short). From a logical perspective, the translator needs to perform two main functions, i.e., feature extraction and style transfer. With consideration of logical network partition, the generator of our EDIT comprises of a part of blocks configured by shared parameters, and the rest by varied parameters exported by an exemplar-domain aware parameter network, for explicitly imitating the functionalities of extraction and mapping. The principle behind this is that, for images from multiple domains, the content features can be obtained by an extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars). In addition, a discriminator is equipped during the training phase to guarantee the output satisfying the distribution of the target domain. Our EDIT can flexibly and effectively work on multiple domains and arbitrary exemplars in a unified neat model. We conduct experiments to show the efficacy of our design, and reveal its advances over other state-of-the-art methods both quantitatively and qualitatively.

Modeling partial lockdowns in multiplex networks using partition strategies

National stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition.