VOLDEMARAS AND SOVIET-LITHUANIAN RELATIONS IN 1926-1929

В статье анализируются нюансы изменений в советско-литовских отношениях после государственного переворота, осуществленного в декабре 1926 г. Приход к власти лидеров Союза литовских националистов (таутининков) А. Сметоны и А. Вольдемараса изначально способствовал возникновению ситуации неопределенности в двусторонних отношениях. В Москве были крайне озабочены стремлением Вольдемараса добиться обострения польско-советских противоречий с целью выгодного для Литвы решения проблемы Вильнюса. Советская сторона испытывала опасения, что Вольдемарас способен спровоцировать вооруженный конфликт с Польшей и что в этот конфликт неизбежно окажется втянут Советский Союз. С другой стороны, советская дипломатия была заинтересована в сохранении как в сохранении независимости Литвы, так и в поддержании некоторой напряженности в отношениях Польши и Литвы, поскольку такая ситуация создавала препятствия для распространения влияния Польши на весь восток Балтики. К концу 1920-х гг. для Москвы стало очевидным, что недовольство политикой Вольдемараса в Литве достигло своего апогея и что следует ждать в ближайшем будущем вытеснение Вольдемараса из политической жизни Литвы. В результате за несколько месяцев до отставки Вольдемараса политическое руководство СССР категорически высказалось против контактов с ним руководства Народного комиссариата по иностранным делам. The article analyzes the nuances of changes in Soviet-Lithuanian relations after the coup d'etat carried out in December 1926. The coming to power of the leaders of the Union of Lithuanian Nationalists (tautininkai) A. Smetona and A. Voldemaras initially contributed to the situation of uncertainty in bilateral relations. Moscow was extremely concerned about Voldemaras' desire to exacerbate the Polish-Soviet contradictions in order to solve the Vilnius problem in Polish-Lithuanian relations. The Soviet diplomats feared that Voldemaras could provoke an armed conflict with Poland and that the Soviet Union would inevitably be involved in the conflict. On the other hand, Soviet diplomacy was interested in maintaining both the Independence of Lithuania and the maintenance of some tension between Poland and Lithuania, as this situation created obstacles to Poland's spread of influence throughout the Baltic East. By the end of the 1920s, it became apparent to Moscow that dissatisfaction with Voldemaras' policy in Lithuania had reached its climax and that Voldemaras should be forced out of Lithuanian political life in the near future. As a result, a few months before Voldemaras' resignation, the political leadership of the USSR categorically opposed contacts with him by the leadership of the People's Commissioner for Foreign Affairs.

Easter Sunday Bombings and Jihad in Sri Lanka

2019 Easter Sunday bombings in Sri Lanka by Islamist radicals poses a level of complexity that could challenge conventional thinking about radicalization and the spread of influence of groups like Al Qaeda, the Islamic State of Iraq and Syria and the Muslim Brotherhood, in many fundamental respects. At a very basic level, it defies common understanding of the emergence of Islamist radicalism in Sri Lanka – a country ravaged by extremist violence in other forms perpetrated by groups like JVP and the LTTE for example, which are mostly secular in character. In this context, jihadism in Sri Lanka introduces a new dynamic - utilitarian and pragmatic - where groups, cutting across their ideological and political divides, come together to achieve common goals. Ability of the groups like ISIS and Muslim Brotherhood to recruit and deploy local Muslims in Sri Lanka to attack Western targets and attract global attention testify to the potency and resiliency of the ideology. [1]

Taxonomy of Influence Maximization Techniques in Unknown Social Networks

Influence maximization in online social networks (OSNs) is the problem of discovering few nodes or users in the social network termed as ‘seed nodes', which can help the spread of influence in the network. With the tremendous growth in social networking, the influence exerted by users of a social network on other online users has caught the attention of researchers to develop effective influence maximization algorithms to be applied in the field of business strategies. The main application of influence maximization is promoting the product to a set of users. However, a real challenge in influence maximization algorithms to deal with enormous amount of users or nodes obtainable in any OSN is posed. The authors focused on graph mining of OSNs for generating ‘seed sets' using standard influence maximization techniques. Many standard influence maximization models are used for calculation of spread of influence; a novel influence maximization technique, namely the DegGreedy technique, has been illustrated along with experimental results to make a comparative analysis of the existing techniques.

A novel approach for fast and effective influence spread maximization in social networks

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

A novel approach for fast and effective influence spread maximization in social networks

The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

Media Leaders Mining Based on Android News Apps

<p align="justify">With the rapid development of the mobile Internet, the mobile news apps have become the most important way for the public to obtain news. As a new media carrier and communication platform,the mobile news apps can promote the rapid dissemination of information and the rapid spread of influence. Some media have a major influence on the direction of other media reports and the behavioral decisions of the public. These media can be regarded as media leaders. Media leaders are very important in the dissemination of news. By identifying media leaders, companies or governments can promote sales or guide public opinion separately. This article believes that media leaders mainly achieve their own influence by publishing news, so this article uses the news published by the mobile news apps as an entry point. This paper firstly solves the problem of data crawling in mobile news apps, and proposes a data crawling method based on reverse analysis, and obtains the data source. Then, reconstruct the reprinting path of the news, and carry out accurate traceability. Finally, cluster the news based on LDA, and propose an algorithm for mining media leaders from three aspects: influence, activity and preference. Experimental studies of data sets have shown that our algorithms can effectively identify media leaders.</p>

Influence Maximization in Social Networks using Deterministic Crowding Algorithm

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.