Rushi Bhatt

Recommendations to boost content spread in social networks

Content sharing in social networks is a powerful mechanism for discovering content on the Internet. The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes. Existing schemes for recommending connections in social networks are based on the number of common neighbors, similarity of user profiles, etc. However, such similarity-based connections do not consider the amount of content discovered. In this paper, we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations. Unlike existing work on influence propagation, in our environment, we are looking for edges instead of nodes, with a bound on the number of incident edges per node. We show that the content spread function is not submodular, and develop approximation algorithms for computing a near-optimal set of edges. Through experiments on real-world social graphs such as Flickr and Twitter, we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections.

Predicting product adoption in large-scale social networks

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. We study the adoption of a paid product by members of a large and well-connected Instant Messenger (IM) network. This product is important to the business and poses unique challenges to advertising due to its low baseline adoption rate. We find that adoption by highly connected individuals is correlated with their social connections (friends) adopting after them. However, there is little evidence of social influence by these high degree individuals. Further, the spread of adoption remains mostly local to first-adopters and their immediate friends. We observe strong evidence of peer pressure wherein future adoption by an individual is more likely if the product has been widely adopted by the individuals friends. Social neighborhoods rich in adoptions also continue to add more new adoptions compared to those neighborhoods that are poor in adoption. Using these insights we build predictive models to identify individuals most suited for two types of marketing campaigns - direct marketing where individuals with highest propensity for future adoption are targeted with suitable ads and social neighborhood marketing which involves messaging to members of the social network who are most effective in using the power of their network to convince their friends to adopt. We identify the most desirable features for predicting future adoption of the PC To Phone product which can in turn be leveraged to effectively promote its adoption. Offline analysis shows that building predictive models for direct marketing and social neighborhood marketing outperforms several widely accepted marketing heuristics. Further, these models are able to effectively combine user features and social features to predict adoption better than using either user features or social features in isolation.

Interactive responses of a thalamic neuron to formalin induced lasting pain in behaving mice.

Thalamocortical (TC) neurons are known to relay incoming sensory information to the cortex via firing in tonic or burst mode. However, it is still unclear how respective firing modes of a single thalamic relay neuron contribute to pain perception under consciousness. Some studies report that bursting could increase pain in hyperalgesic conditions while others suggest the contrary. However, since previous studies were done under either neuropathic pain conditions or often under anesthesia, the mechanism of thalamic pain modulation under awake conditions is not well understood. We therefore characterized the thalamic firing patterns of behaving mice in response to nociceptive pain induced by inflammation. Our results demonstrated that nociceptive pain responses were positively correlated with tonic firing and negatively correlated with burst firing of individual TC neurons. Furthermore, burst properties such as intra-burst-interval (IntraBI) also turned out to be reliably correlated with the changes of nociceptive pain responses. In addition, brain stimulation experiments revealed that only bursts with specific bursting patterns could significantly abolish behavioral nociceptive responses. The results indicate that specific patterns of bursting activity in thalamocortical relay neurons play a critical role in controlling long-lasting inflammatory pain in awake and behaving mice.

Adaptive policies for selecting groupon style chunked reward ads in a stochastic knapsack framework

Stochastic knapsack problems deal with selecting items with potentially random sizes and rewards so as to maximize the total reward while satisfying certain capacity constraints. A novel variant of this problem, where items are worthless unless collected in bundles, is introduced here. This setup is similar to the Groupon model, where a deal is off unless a minimum number of users sign up for it. Since the optimal algorithm to solve this problem is not practical, several adaptive greedy approaches with reasonable time and memory requirements are studied in detail - theoretically, as well as, experimentally. Worst case performance guarantees are provided for some of these greedy algorithms, while results of experimental evaluation demonstrate that they are much closer to optimal than what the theoretical bounds suggest. Applications include optimizing for online advertising pricing models where advertisers pay only when certain goals, in terms of clicks or conversions, are met. We perform extensive experiments for the situation where there are between two and five ads. For typical ad conversion rates, the greedy policy of selecting items having the highest individual expected reward obtains a value within 5% of optimal over 95% of the time for a wide selection of parameters.

Online Advertising in Social Networks

Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. This chapter focuses on the role of social network information in online display advertising.

α-Calcium calmodulin kinase II modulates the temporal structure of hippocampal bursting patterns.

The alpha calcium calmodulin kinase II (α-CaMKII) is known to play a key role in CA1/CA3 synaptic plasticity, hippocampal place cell stability and spatial learning. Additionally, there is evidence from hippocampal electrophysiological slice studies that this kinase has a role in regulating ion channels that control neuronal excitability. Here, we report in vivo single unit studies, with α-CaMKII mutant mice, in which threonine 305 was replaced with an aspartate (α-CaMKIIT305D mutants), that indicate that this kinase modulates spike patterns in hippocampal pyramidal neurons. Previous studies showed that α-CaMKIIT305D mutants have abnormalities in both hippocampal LTP and hippocampal-dependent learning. We found that besides decreased place cell stability, which could be caused by their LTP impairments, the hippocampal CA1 spike patterns of α-CaMKIIT305D mutants were profoundly abnormal. Although overall firing rate, and overall burst frequency were not significantly altered in these mutants, inter-burst intervals, mean number of intra-burst spikes, ratio of intra-burst spikes to total spikes, and mean intra-burst intervals were significantly altered. In particular, the intra burst intervals of place cells in α-CaMKIIT305D mutants showed higher variability than controls. These results provide in vivo evidence that besides its well-known function in synaptic plasticity, α-CaMKII, and in particular its inhibitory phosphorylation at threonine 305, also have a role in shaping the temporal structure of hippocampal burst patterns. These results suggest that some of the molecular processes involved in acquiring information may also shape the patterns used to encode this information.