The patent citation count is a good indicator of patent quality.This often generates monetary value for the inventors and organizations.However, the factors that influence a patent receiving high citations over the year are still not well understood.With the patents over the past two decades, we study the problem of patent citation prediction and formulate this as a binary classification problem.We create a semantic graph of patents based on their semantic similarities, enabling the use of Graph Neural Network (GNN)-based approaches for predicting citations.Our experimental results demonstrate the effectiveness of our GNN-based methods when applied to the semantic graph, showing that they can accurately predict patent citations using only patent text.More specifically, these methods produce up to 94% recall for patents with high citations and outperform existing baselines.Furthermore, we leverage this constructed graph to gain insights and explanations for the predictions made by the GNNs.

Extensive literature argues that crowds possess essential collective intelligence benefits that allow superior decision-making by untrained individuals working in low-information environments. Classic wisdom of crowds theory is based on evidence gathered from studying large groups of diverse and independent decision-makers. Yet, most human decisions are reached in online settings of interconnected like-minded people that challenge these criteria. This observation raises a key question: Are there surprising expressions of collective intelligence online? Here, we explore whether crowds furnish collective intelligence benefits in crowdfunding systems. Crowdfunding has grown and diversified quickly over the past decade, expanding from funding aspirant creative works and supplying pro-social donations to enabling large citizen-funded urban projects and providing commercial interest-based unsecured loans. Using nearly 10 million loan contributions from a market-dominant lending platform, we find evidence for collective intelligence indicators in crowdfunding. Our results, which are based on a two-stage Heckman selection model, indicate that opinion diversity and the speed at which funds are contributed predict who gets funded and who repays, even after accounting for traditional measures of creditworthiness. Moreover, crowds work consistently well in correctly assessing the outcome of high-risk projects. Finally, diversity and speed serve as early warning signals when inferring fundraising based solely on the initial part of the campaign. Our findings broaden the field of crowd-aware system design and inform discussions about the augmentation of traditional financing systems with tech innovations.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

No abstract or record of the panel discussion was made available for publication as part of the conference proceedings.

Summary form only given. The complete presentations were not made available for publication as part of the conference proceedings.These Keynotes speeches the following: Ideas and Inventions; Scalable Collective Reasoning for Richly Structured Socio-Behavioral Data; Computational Methods for Team Formation and Network structure and information diffusion.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network's structure and communicative properties. Here, we study how external events are associated with a network's change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness. Rather, the network 'turtles up'. It displays a propensity for higher clustering, strong tie inter- action, and an intensification of insider vs. outsider communication. Further, we find changes in network structure pre- dict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Crowds offer a new form of efficacious collective decision making, yet knowledge about the mechanisms by which they achieve superior outcomes remains nascent. It has been suggested that crowds work best with market-like relationships when individuals make independent decisions and possess dissimilar information. By contrast, sociological discussions of markets argue that risky decisions are mitigated by network relations that embed economic transactions in social ties that promote trustworthiness and reciprocity. To investigate the role of networks within crowds and their performance effects, we examined the complete record of financial lending decisions on Prosper.com, 1/2006-3/2012, the first U.S. crowdfunding platform and a chief gateway to capital for entrepreneurs and general borrowers that continues to disrupt conventional financial lending structures infusing more than $5.1 billion into the market in 2013. Our study reveals how reciprocity, recurring borrower-lender dyads, and persistent co-lending underpin the dynamics of network lending. Further, we show how network ties influence the evolution of the lending behavior. We find that in the early stage of fundraising, network relations provide larger proportions of loans, typically lending four times more per bid than strangers. They also respond to loan requests on average 59.5% sooner than strangers. The size of the first loan and the time to lending also tend to prompt lending by strangers, suggesting that network relations might move the market, a finding that persists even as fewer lenders dominate more of the market for loans on Prosper. Finally, network relations are associated with greater engagement: when the first loan is underwritten by a friend, 50% of the remaining loans come from friends as well.