Seeding with Costly Network Information

We study the task of selecting $k$ nodes in a social network of size $n$, to seed a diffusion with maximum expected spread size, under the independent cascade model with cascade probability $p$. Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed set with provable guarantees, given the knowledge of the entire network. However, in practice, obtaining full knowledge of the network is very costly. To address this gap, we first study the achievable guarantees using $o(n)$ influence samples. We provide an approximation algorithm with a tight (1-1/e){\mbox{OPT}}-\epsilon n guarantee, using $O_{\epsilon}(k^2\log n)$ influence samples and show that this dependence on $k$ is asymptotically optimal. We then propose a probing algorithm that queries ${O}_{\epsilon}(p n^2\log^4 n + \sqrt{k p} n^{1.5}\log^{5.5} n + k n\log^{3.5}{n})$ edges from the graph and use them to find a seed set with the same almost tight approximation guarantee. We also provide a matching (up to logarithmic factors) lower-bound on the required number of edges. To address the dependence of our probing algorithm on the independent cascade probability $p$, we show that it is impossible to maintain the same approximation guarantees by controlling the discrepancy between the probing and seeding cascade probabilities. Instead, we propose to down-sample the probed edges to match the seeding cascade probability, provided that it does not exceed that of probing. Finally, we test our algorithms on real world data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding strategies

A Theory of Strategic Diffusion

The important role of friends, neighbors and colleagues in shaping individual choices has been brought out in a number of studies over the years. The presence of significant 'local' influence in shaping individual behavior suggests that firms, governments and developmental agencies should explicitly incorporate it in the design of their marketing and developmental strategies. This paper develops a framework for the study of optimal strategies in the presence of social interaction. We focus on the case of a single player who exerts costly effort to get a set of individuals � engaged in social interaction � to choose a certain action. Our formulation allows for different types of social interaction and also allows for the player to have incomplete information concerning the connections among individuals. We first show that incorporating information on social interaction can have large effects on the profits of a player. Then, we establish that an increase in the level and dispersion of social interaction can raise or lower the optimal strategy and profits of the player, depending on the content of the interaction. Finally, we study the value of social network information for the player and find that it depends on the dispersion in social connections. The economic interest of these results is illustrated via a discussion of two economic applications: advertising in the presence of word of mouth communication and seeding a network.

A Theory of Strategic Diffusion

The important role of friends, neighbors and colleagues in shaping individual choices has been brought out in a number of studies over the years. The presence of significant ‘local’ influence in shaping individual behavior suggests that firms, governments and developmental agencies should explicitly incorporate it in the design of their marketing and developmental strategies. This paper develops a framework for the study of optimal strategies in the presence of social interaction. We focus on the case of a single player who exerts costly effort to get a set of individuals – engaged in social interaction – to choose a certain action. Our formulation allows for different types of social interaction (ranging from sharing of information to direct adoption externalities) and also allows for the player to have incomplete information concerning the connections among individuals. The analysis starts by showing that incorporating information on social interaction can have large effects on the profits of a player. We then show that an increase in the level and dispersion of social interaction can raise or lower the optimal strategy and profits of the player, depending on the content of the interaction. We then study the value of social network information for the player and find that it depends on the dispersion in social connections. The economic interest of these results is illustrated via a discussion of two economic applications: advertising in the presence of word of mouth communication and seeding a network.Social Interaction, Seeding the Network, Word of Mouth Communication, Diffusion Strategy

Studying Diffusion of Viral Content at Dyadic Level

Diffusion of information and viral content, social contagion and influence are still topics of broad evaluation. As theory explaining the role of influentials moves slightly to reduce their importance in the propagation of viral content, authors of the following paper have studied the information epidemic in a social networking platform in order to confirm recent theoretical findings in this area. While most of related experiments focus on the level of individuals, the elementary entities of the following analysis are dyads. The authors study behavioral motifs that are possible to observe at the dyadic level. The study shows significant differences between dyads that are more vs less engaged in the diffusion process. Dyads that fuel the diffusion proccess are characterized by stronger relationships (higher activity, more common friends), more active and networked receiving party (higher centrality measures), and higher authority centrality of person sending a viral message.Comment: ASONAM 2012, The 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, pp. 1291-129

Seed Sector Evolution in Zambia and Zimbabwe: Has Farmer Access Improved Following Economic Reforms?

Crop Production/Industries, Downloads July 2008-June 2009: 19,

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.Comment: ECCV 201