Network Analysis and Actors #CancelOmnibusLaw on Twitter Social Media Using Social Network Analysis (SNA)

Omnibus Law is the merging of several different rules into one law. RUU Cipta Kerja is one part of the Omnibus Law that attracts attention because it is considered detrimental to society. This caused a lot of rejection and protests from the society. The protest was held directly in the form of demonstrations in various regions of Indonesia and also in Twitter through #BatalkanOmnibusLaw. The purpose of this research is to find out the analysis of communication networks and identify influential actors in #BatalkanOmnibusLaw on Twitter. This research uses Social Network Analysis (SNA) methods and Computer-mediated Communication theory. Data is collected through Twitter from August 1-October 31, 2020. The process of analyzing and retrieving data is using Netlytic.org and Gephi software. The results showed that there were 62 actors with 153 interactions. Proximity between actors is worth 3, meaning close proximity and easy interaction between actors. The interactions created between actors are very few, uneven ,and the interactions that occur only one way. The #BatalkanOmnibusLaw is centered on ten actors, the most dominant account is @fraksirakyatid. Based on degree centrality analysis, closeness centrality, betweenness centrality, and eigenvector centrality the most influential actors in #BatalkanOmnibusLaw network are @fraksirakyatid and @walhinasional. Keywords: #BatalkanOmnibusLaw, Twitter, Actor, Communication Network

Allosteric Determinants of the SARS-CoV-2 Spike Protein Binding with Nanobodies: Examining Mechanisms of Mutational Escape and Sensitivity of the Omicron Variant

Structural and biochemical studies have recently revealed a range of rationally engineered nanobodies with efficient neutralizing capacity against SARS-CoV-2 virus and resilience against mutational escape. In this study, we performed a comprehensive computational analysis of the SARS-CoV-2 spike trimer complexes with Nb6, VHH E and bi-paratopic VHH VE nanobodies. We combined atomistic dynamics and collective motions analysis with binding free energy scanning, perturbation-response scanning and network centrality analysis to examine mechanisms of nanobody-induced allosteric modulation and cooperativity in the SARS-CoV-2 spike trimer complexes with these nanobodies. By quantifying energetic and allosteric determinants of the SARS-CoV-2 spike protein binding with nanobodies, we also examined nanobody-induced modulation of escaping mutations and the effect of the Omicron variant on nanobody binding. The mutational scanning analysis supported the notion that E484A mutation can have a significant detrimental effect on nanobody binding and result in Omicron-induced escape from nanobody neutralization. Our findings showed that SARS-CoV-2 spike protein may exploit plasticity of specific allosteric hotspots to generate escape mutants that alter response to binding without compromising activity. The network analysis supported these findings showing that VHH VE nanobody binding can induce long-range couplings between the cryptic binding epitope and ACE2-binding site through a broader ensemble of communication paths that is less dependent on specific mediating centers and therefore may be less sensitive to mutational perturbations of functional residues. The results suggest that binding affinity and long-range communications of the SARS-CoV-2 complexes with nanobodies can be determined by structurally stable regulatory centers and conformationally adaptable hotspots that are allosterically coupled and collectively control resilience to mutational escape.

How Does Travel Demand Follow the Change in Infrastructure? Multiple-Year Eigenvector Centrality Analysis

The road network is one of the most permanent elements of the physical structure of cities, and the long-term impacts should be considered for effective and efficient road network improvement. It is therefore important to catch up on how the road will be used after construction. However, we do not have much knowledge on the pattern and time lag in the change process of travel demand and supply in the real situation. To explore such changes, this study proposes to evaluate a network with eigenvector centrality (EC) measures that can evaluate the importance of nodes in a network. We believe the analysis based on topological properties by the graph theory is suitable to verify the evolution of road networks. This study analysed long-term changes over 20 years in an actual city to understand the impact of road network improvements. The EC analysis with the weights of traffic indices obtained from survey data evaluates the connectivity of road services on the supply side, and traffic concentration on the demand side.

Hotspots for mutations in the SARS-CoV-2 spike glycoprotein: a correspondence analysis

Spike glycoprotein (Sgp) is liable for binding of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the host receptors. Since Sgp is the main target for vaccine and drug designing, elucidating its mutation pattern could help in this regard. This study is aimed at investigating the correspondence of specific residues to the SgpSARS-CoV-2 functionality by explorative interpretation of sequence alignments. Centrality analysis of the Sgp dissects the importance of these residues in the interaction network of the RBD-ACE2 (receptor-binding domain) complex and furin cleavage site. Correspondence of RBD to threonine500 and asparagine501 and furin cleavage site to glutamine675, glutamine677, threonine678, and alanine684 was observed; all residues are exactly located at the interaction interfaces. The harmonious location of residues dictates the RBD binding property and the flexibility, hydrophobicity, and accessibility of the furin cleavage site. These species-specific residues can be assumed as real targets of evolution, while other substitutions tend to support them. Moreover, all these residues are parts of experimentally identified epitopes. Therefore, their substitution may affect vaccine efficacy. Higher rate of RBD maintenance than furin cleavage site was predicted. The accumulation of substitutions reinforces the probability of the multi-host circulation of the virus and emphasizes the enduring evolutionary events.

Revealing the component structure of the world air transportation network

Air transportation plays an essential role in the global economy. Therefore, there is a great deal of work to understand better the complex network formed by the links between the origins and destinations of flights. Some investigations show that the world air transportation network exhibits a community and a core-periphery structure. Although precious, these representations do not distinguish the inter-regional (global) web of connections from the regional (local) one. Therefore, we propose a new mesoscopic model called the component structure that decomposes the network into local and global components. Local components are the dense areas of the network, and global components are the nodes and links bridging the local components. As a case study, we consider the unweighted and undirected world air transportation network. Experiments show that it contains seven large local components and multiple small ones spatially well-defined. Moreover, it has a main global component covering the world. We perform an extensive comparative analysis of the structure of the components. Results demonstrate the non-homogeneous nature of the world air transportation network. The local components structure highlights regional differences, and the global component organization captures the efficiency of inter-regional travel. Centrality analysis of the components allows distinguishing airports centered on regional destinations from those focused on inter-regional exchanges. Core analysis is more accurate in the components than in the whole network where Europe dominates, blurring the rest of the world. Besides the world air transportation network, this paper demonstrates the potential of the component decomposition for modeling and analyzing the mesoscale structure of networks.

Penentuan Klaster Wilayah Kecamatan Berdasarkan Pusat Pelayanan Masyarakat di Wilayah Kepulauan Kabupaten Maluku Tengah Provinsi Maluku

The connectivity of a region is an indicator the growth of service centers. Community service is carried out with a team from PT. Maluku and North Maluku Regional Development Bank Central Maluku Branch during 2018. Data were collected in 18 districts in Central Maluku Regency through the Rapid Survey Method for regional potential and socio-economic dynamics of the community. Central Maluku Regency BPS data support 2018 is a complementary input in the centrality analysis. The data were processed using quantitative descriptive analysis using Guttman scale/Marshall Centrality Index and spatial analysis tools using ArcmapGIS 10.3.1 software, to obtain a map of the regional service center. Central Maluku Regency is an archipelago, having district clusters spread over Seram Island, Ambon, Lease Islands and Banda. The district cluster is divided into 4 regions with the highest level of regional service being in Amahei District (11.14) and the lowest in Nusa Laut (2.50). Economic growth in sub-districts in the archipelago experienced a positive trend, concentrated on Ambon Island and Banda Islands, while the Lease Islands centrality index was in the lower middle category.

Multi-mode Network Analysis under Differential Privacy

With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.

Study on Business Ecosystem Research Trend Using Network Text Analysis

The management environment is moving into a new phase with the changing global circumstances. The business ecosystem as a management strategy has been studied for the last 30 years since the concept was introduced. The purpose of this study was to analyze the research trend in business ecosystem by using network next analysis and to understand the concept, being one that is still being actively studied. Network text analysis is a commonly used method to analyze research trends by forming networks based on bibliographic data of the articles, namely, keywords. For the analysis, we collected the data and keywords from 340 research papers published in global academic journals related to business ecosystem on the basis of the Scopus database. Through keywords extraction and cleansing, we found that the keywords of “innovation”, “sustainability”, and “platform” were mentioned most frequently, and the research topics were correlated to each other. Moreover, we conducted degree centrality and betweenness centrality analysis along with clustering analysis by transforming the two-mode network into a one-mode network. Degree centrality involves analyzing the degree to which one keyword links to other keywords, and betweenness centrality shows the mediating effects of a keyword to other keywords. In the centrality analysis results, “innovation”, “sustainability”, “platform”, and “business model” showed the highest degree centrality, and “sustainability”, “innovation”, “China”, and “platform” had the highest betweenness centrality. Then, we classified the clusters of subtopics into five groups. The current study examined accumulated research and suggested a comprehensive understanding of the research trend in business ecosystem by incorporating a method enabling research trend analysis to secure objectivity. This research is expected to help researchers to review the research trend in business ecosystem and identify expandable topics for further studies.

Accuracy of Regional Centrality Using Social Network Analysis: Evidence from Commuter Flow in South Korea

With the recent exponential growth in inter-regional movements of population and information, there is an urgent need for accurately measuring the connectivity and centrality of cities. This study aims to investigate the differences in centrality between different scales of a dataset and to propose a calibration method to minimize the gap between the measures from the two scales. Although urban and regional centrality is examined by analyzing regional commuting datasets, this study proposes that it should be measured using nationwide data to validate the centrality results. To demonstrate this, the differences in regional centrality between different spatial scales of commuting trips for two data groups are shown: Seoul regional data and nationwide data. In this structure, the centrality levels of the 25 districts of Seoul were calculated for both groups. The results clearly show the differences in the centrality levels of districts in both groups: Seongbuk district ranked 10th in the local dataset but fell to 18th in the nationwide dataset; Geumcheon district ranked 22nd in the former but rose to 9th in the latter. The ratio of inner commuting in Seoul is thus relatively low, and each district has dynamic connections with other provinces. Furthermore, the results of a linear regression analysis, which was conducted on a local dataset to obtain similar results as those obtained using a national dataset, demonstrate the significance of a wide-ranging commuting dataset for regional centrality analysis of a specific region.