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"This important new book fills a large gap in legal literature by examining restitution in private international law,including both the jurisdiction and choice of law questions facing restitutionary claims with international elements. The book begins with a brief summary of the English domestic law of restitution and highlights some of the issues which may arise. It goes on to examine classification, or characterisation of restitutionary claims. Restitution has a theoretical unity which enables the author to treat it essentially as a single issue for characterisation purposes. However, restitutionary claims arise in the context of contracts and wrongs; they may be at law or in equity; they may give rise to personal or proprietary remedies, whilst they may be contingent on tracing. Each of these contexts is analysed separately for the purposes of characterisation. The central part of the book examines the choice of law rule for restitutionary issues, and reviews the different approaches adopted in the US and UK and in other parts of the common law world. After weighing the merits of the different approaches the author adopts a choice of law rule for restitutionary issues which is the proper law of the unjust factor. Depending on whether the unjust factor is event-based or law-based, the choice of law rule will focus on either the law of the place, or alternatively, the legal system with which the unjust factor has its closest and most real connection. Jurisdiction is an area of increasing importance in private international law and the book provides a thorough analysis of the topic of jurisdiction for restitutionary claims, both under the Brussels Convention as well as the traditional common law rules contained in the Civil Procedure Rules. This is an important and timely new work for all lawyers interested in restitution, private international law and international commercial litigation."--Bloomsbury Publishing

International audience ; The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for online political propaganda etc. Though these algorithms have been developed for many years, the majority of the literature focuses on scalability issues and relaxing the method's assumptions. In the recent years, the emergence of new complementary data and more advanced machine learning methods for decision have guided part of the literature towards learning-based approaches. These can range from learning how information spreads over a network, to learning how to solve the combinatorial optimization problem itself. In this tutorial, we aim to dissentangle and clearly define the different tasks around learning for influence applications in social networks. More specifically, we start from traditional influence maximization algorithms, describe the need of influence estimation and delineate the state-of-the-art on influence and diffusion learning. Subsequently, we delve into the problem of learning while optimizing the influence spreading which is based on online learning algorithms. Finally, we describe the latest approaches on learning influence maximization with graph neural networks and deep reinforcement learning.

International audience ; The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for online political propaganda etc. Though these algorithms have been developed for many years, the majority of the literature focuses on scalability issues and relaxing the method's assumptions. In the recent years, the emergence of new complementary data and more advanced machine learning methods for decision have guided part of the literature towards learning-based approaches. These can range from learning how information spreads over a network, to learning how to solve the combinatorial optimization problem itself. In this tutorial, we aim to dissentangle and clearly define the different tasks around learning for influence applications in social networks. More specifically, we start from traditional influence maximization algorithms, describe the need of influence estimation and delineate the state-of-the-art on influence and diffusion learning. Subsequently, we delve into the problem of learning while optimizing the influence spreading which is based on online learning algorithms. Finally, we describe the latest approaches on learning influence maximization with graph neural networks and deep reinforcement learning.

International audience ; The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for online political propaganda etc. Though these algorithms have been developed for many years, the majority of the literature focuses on scalability issues and relaxing the method's assumptions. In the recent years, the emergence of new complementary data and more advanced machine learning methods for decision have guided part of the literature towards learning-based approaches. These can range from learning how information spreads over a network, to learning how to solve the combinatorial optimization problem itself. In this tutorial, we aim to dissentangle and clearly define the different tasks around learning for influence applications in social networks. More specifically, we start from traditional influence maximization algorithms, describe the need of influence estimation and delineate the state-of-the-art on influence and diffusion learning. Subsequently, we delve into the problem of learning while optimizing the influence spreading which is based on online learning algorithms. Finally, we describe the latest approaches on learning influence maximization with graph neural networks and deep reinforcement learning.

International audience ; The problem of maximizing or minimizing the spreading in a social network has become more timely than ever with the advent of fake news and the coronavirus epidemic. The solution to this problem pertains to influence maximization algorithms that identify the right nodes to lockdown for epidemic containment, hire for viral marketing campaigns, block for online political propaganda etc. Though these algorithms have been developed for many years, the majority of the literature focuses on scalability issues and relaxing the method's assumptions. In the recent years, the emergence of new complementary data and more advanced machine learning methods for decision have guided part of the literature towards learning-based approaches. These can range from learning how information spreads over a network, to learning how to solve the combinatorial optimization problem itself. In this tutorial, we aim to dissentangle and clearly define the different tasks around learning for influence applications in social networks. More specifically, we start from traditional influence maximization algorithms, describe the need of influence estimation and delineate the state-of-the-art on influence and diffusion learning. Subsequently, we delve into the problem of learning while optimizing the influence spreading which is based on online learning algorithms. Finally, we describe the latest approaches on learning influence maximization with graph neural networks and deep reinforcement learning.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.