Complex networks have been studied intensively for a decade, but research still focuses on the limited case of a single, non-interacting network. Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks. This may happen recursively and can lead to a cascade of failures. In fact, a failure of a very small fraction of nodes in one network may lead to the complete fragmentation of a system of several interdependent networks. A dramatic real-world example of a cascade of failures (‘concurrent malfunction’) is the electrical blackout that affected much of Italy on 28 September 2003: the shutdown of power stations directly led to the failure of nodes in the Internet communication network, which in turn caused further breakdown of power stations. Here we develop a framework for understanding the robustness of interacting networks subject to such cascading failures. We present exact analytical solutions for the critical fraction of nodes that, on removal, will lead to a failure cascade and to a complete fragmentation of two interdependent networks. Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves. Our findings highlight the need to consider interdependent network properties in designing robust networks.

http://absimage.aps.org/image/MAR10/MWS_MAR10-2009-001087.pdf

Catastrophic cascade of failures in interdependent networks

Abstract Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system. 

/pdf/a-systematic-review-and-research-perspective-on-recommender-1elyl9a4.pdf

A systematic review and research perspective on recommender systems

https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-022-00592-5

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis.

/pdf/graph-representation-of-patient-data-a-systematic-literature-x58sonz14p.pdf

Graph-Representation of Patient Data: a Systematic Literature Review.

Energy is a vital resource in pervasive computing. Remote execution, a static approach to energy saving of mobile devices, is not applicable to the constantly varying environment in pervasive computing. This paper presents a dynamic software configuration approach to minimizing energy consumption by moving or/and replicating the appropriate components of an application among the machines. After analyzing three types of energy costs of the distributed applications, we set up a math optimization model of energy consumption. Based on the graph theory, the optimization problem of energy cost can be transformed into the Min-cut problem of a cost graph. Then, we propose two novel optimal software allocation algorithms for saving power. The first makes use of component migration to reasonably allocate the components among the machines at runtime, and the second is to replicate some components among machines to further save more energy than component migration. The simulations reveal that the two proposed algorithms can effectively save energy of mobile devices, and obtain better performance than the previous approaches in most of cases.

Energy saving of mobile devices based on component migration and replication in pervasive computing

In any under served rural areas, the role of health workers is very important because most of the people are used to get their medical assistance from these health workers. They assist the people to detect their disease or illness against some set of symptoms and information related to the symptoms. Educational qualifications of the health workers are very low i.e. up to the 10th standard and therefore, the lack of medical knowledge and training are the main obstacle to identify the disease properly. In mobile-assisted remote health-care system, the primary objective is to help the front-line health workers to extend their interactions, to increase their medical assistance in an effective and efficient way. In this paper, we propose a mobile-assisted diagnosis scheme which helps the patients and health workers to access the reference guideline or manuals, investigations, medical records using mobile devices. Further, for storing the symptoms of different diseases and related information with the symptoms, a data model is also designed. This data model is named as Disease-Symptom data model and it is implemented in Neo4j graph database. Some queries are tested in this data model and resulted graphs and sub-graphs are shown here.

Mobile-assisted remote healthcare delivery

Building a trust-based doctor recommendation system on top of multilayer graph database.

In the rural or remote areas of India, most people are still used to get their medical treatment or assistance from Community Health Center where ANM (Auxiliary Nurse Mid-wife) or ASHA (Accredited Social Health Activist) workers are active healthcare service providers. But they are not sufficiently trained. In this paper, we developed MoRe-Care (Mobile-Assisted Remote Healthcare Service): a mobile app for Android mobile device. It is implemented based on our earlier proposed scheme which is unique, affordable, effective mobile assisted remote healthcare service delivery scheme. This scheme assists the ANM and ASHA workers and direct them with proper guidelines.

MoRe-care: Mobile-assisted remote healthcare service delivery

Breadth-first search (BFS) is one of the most fundamental algorithms for searching a graph. In our previous work, a mobile-assisted diagnosis scheme has been proposed and we have designed a Disease-Symptom data model as a large knowledge base. In this work, we present the design and implementation of a pruning algorithm to capture a part of the large graph-based data model. Generally, to search a large graph, most of the graph queries are too long and very cumbersome to write and sometimes very difficult to implement. Pruning algorithm is one of the prominent solutions of this problem. It results a subgraph or forest for the desired input. Here, our proposed pruning algorithm is multi-source sequential BFS algorithm and it is demonstrated on Disease-Symptom graph database.

A BFS-Based Pruning Algorithm for Disease-Symptom Knowledge Graph Database

Information and Communication Technologies (ICT) provides access to sources of knowledge or health information through technologies like Internet, wireless network, mobile phones etc. Primary healthcare delivery based on mobile devices can achieve the goal of “health for all” and meet the requirements for people's basic health through a comprehensive approach. This paper proposes a framework for ICT based primary healthcare delivery from remote places particularly for regular supervision for neonatal care of pregnant woman to assist diagnosis of diseases of children, and immunization of children. These three main common primary healthcare services may address the problem of highest mortality rate according to National Rural Health Mission (NHRM). However, a lot of challenges are faced to implement these services. The workflow or activity diagrams are shown for the implementation of diagnosis assistance and neonatal care of pregnant woman and immunization of children.

Safikureshi Mondal

Papers

Mobile-assisted remote healthcare delivery

Building a trust-based doctor recommendation system on top of multilayer graph database.

MoRe-care: Mobile-assisted remote healthcare service delivery

A BFS-Based Pruning Algorithm for Disease-Symptom Knowledge Graph Database

A framework for ICT-based primary healthcare delivery for children