arXiv subjects

Xiaodan Lou

Publications and source records attributed to Xiaodan Lou.

4 recordsLinked to original sources

arXiv2019

Link Prediction via Graph Attention Network

Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications. However, conventional link prediction approaches neither have high prediction accuracy nor being capable of revealing the hidden information behind links. To address this problem, we generalize the latest techniques in deep learning on graphs and present a new link prediction model - DeepLinker. Instead of learning node representation with the node label information, DeepLinker uses the links as supervised information. Experiments on five graphs show that DeepLinker can not only achieve the state-of-the-art link prediction accuracy, but also acquire the efficient node representations and node centrality ranking as the byproducts. Although the representations are obtained without any supervised node label information, they still perform well on node ranking and node classification tasks.

cs.SI↗

arXiv2019

Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics

Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a hallmark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.

cs.CY↗

arXiv2019

Qualifying threshold of take off stage for successfully disseminated creative ideas

The creative process is essentially Darwinian and only a small proportion of creative ideas are selected for further development. However, the threshold that identifies this small fraction of successfully disseminated creative ideas at their early stage has not been thoroughly analyzed through the lens of Rogers innovation diffusion theory. Here, we take highly cited (top 1%) research papers as an example of the most successfully disseminated creative ideas and explore the time it takes and citations it receives at their take off stage, which play a crucial role in the dissemination of creativity. Results show the majority of highly cited papers will reach 10% and 25% of their total citations within two years and four years, respectively. Interestingly, our results also present a minimal number of articles that attract their first citation before publication. As for the discipline, number of references, and Price index, we find a significant difference exists: Clinical, Pre-Clinical & Health and Life Sciences are the first two disciplines to reach the C10% and C25% in a shorter amount of time. Highly cited papers with limited references usually take more time to reach 10% and 25% of their total citations. In addition, highly cited papers will attract citations rapidly when they cite more recent references. These results provide insights into the timespan and citations for a research paper to become highly cited at the take off stage in its diffusion process, as well as the factors that may influence it.

cs.DL↗

arXiv2015

Flow Distances on Open Flow Networks

Open flow network is a weighted directed graph with a source and a sink, depicting flux distributions on networks in the steady state of an open flow system. Energetic food webs, economic input-output networks, and international trade networks, are open flow network models of energy flows between species, money or value flows between industrial sectors, and goods flows between countries, respectively. Flow distances (first-passage or total) between any given two nodes $i$ and $j$ are defined as the average number of transition steps of a random walker along the network from $i$ to $j$ under some conditions. They apparently deviate from the conventional random walk distance on a closed directed graph because they consider the openness of the flow network. Flow distances are explicitly expressed by underlying Markov matrix of a flow system in this paper. With this novel theoretical conception, we can visualize open flow networks, calculating centrality of each node, and clustering nodes into groups. We apply flow distances to two kinds of empirical open flow networks, including energetic food webs and economic input-output network. In energetic food webs example, we visualize the trophic level of each species and compare flow distances with other distance metrics on graph. In input-output network, we rank sectors according to their average distances away other sectors, and cluster sectors into different groups. Some other potential applications and mathematical properties are also discussed. To summarize, flow distance is a useful and powerful tool to study open flow systems.

physics.soc-ph↗