25,531 research outputs found
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks
Nowadays, many network representation learning algorithms and downstream
network mining tasks have already paid attention to dynamic networks or
temporal networks, which are more suitable for real-world complex scenarios by
modeling evolving patterns and temporal dependencies between node interactions.
Moreover, representing and mining temporal networks have a wide range of
applications, such as fraud detection, social network analysis, and drug
discovery. To contribute to the network representation learning and network
mining research community, in this paper, we generate a new biological dataset
of dynamic protein-protein interaction networks (i.e., DPPIN), which consists
of twelve dynamic protein-level interaction networks of yeast cells at
different scales. We first introduce the generation process of DPPIN. To
demonstrate the value of our published dataset DPPIN, we then list the
potential applications that would be benefited. Furthermore, we design dynamic
local clustering, dynamic spectral clustering, dynamic subgraph matching,
dynamic node classification, and dynamic graph classification experiments,
where DPPIN indicates future research opportunities for some tasks by
presenting challenges on state-of-the-art baseline algorithms. Finally, we
identify future directions for improving this dataset utility and welcome
inputs from the community. All resources of this work are deployed and publicly
available at https://github.com/DongqiFu/DPPIN
The Size Conundrum: Why Online Knowledge Markets Can Fail at Scale
In this paper, we interpret the community question answering websites on the
StackExchange platform as knowledge markets, and analyze how and why these
markets can fail at scale. A knowledge market framing allows site operators to
reason about market failures, and to design policies to prevent them. Our goal
is to provide insights on large-scale knowledge market failures through an
interpretable model. We explore a set of interpretable economic production
models on a large empirical dataset to analyze the dynamics of content
generation in knowledge markets. Amongst these, the Cobb-Douglas model best
explains empirical data and provides an intuitive explanation for content
generation through concepts of elasticity and diminishing returns. Content
generation depends on user participation and also on how specific types of
content (e.g. answers) depends on other types (e.g. questions). We show that
these factors of content generation have constant elasticity---a percentage
increase in any of the inputs leads to a constant percentage increase in the
output. Furthermore, markets exhibit diminishing returns---the marginal output
decreases as the input is incrementally increased. Knowledge markets also vary
on their returns to scale---the increase in output resulting from a
proportionate increase in all inputs. Importantly, many knowledge markets
exhibit diseconomies of scale---measures of market health (e.g., the percentage
of questions with an accepted answer) decrease as a function of number of
participants. The implications of our work are two-fold: site operators ought
to design incentives as a function of system size (number of participants); the
market lens should shed insight into complex dependencies amongst different
content types and participant actions in general social networks.Comment: The 27th International Conference on World Wide Web (WWW), 201
When is a Network a Network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks
We introduce a framework for the modeling of sequential data capturing
pathways of varying lengths observed in a network. Such data are important,
e.g., when studying click streams in information networks, travel patterns in
transportation systems, information cascades in social networks, biological
pathways or time-stamped social interactions. While it is common to apply graph
analytics and network analysis to such data, recent works have shown that
temporal correlations can invalidate the results of such methods. This raises a
fundamental question: when is a network abstraction of sequential data
justified? Addressing this open question, we propose a framework which combines
Markov chains of multiple, higher orders into a multi-layer graphical model
that captures temporal correlations in pathways at multiple length scales
simultaneously. We develop a model selection technique to infer the optimal
number of layers of such a model and show that it outperforms previously used
Markov order detection techniques. An application to eight real-world data sets
on pathways and temporal networks shows that it allows to infer graphical
models which capture both topological and temporal characteristics of such
data. Our work highlights fallacies of network abstractions and provides a
principled answer to the open question when they are justified. Generalizing
network representations to multi-order graphical models, it opens perspectives
for new data mining and knowledge discovery algorithms.Comment: 10 pages, 4 figures, 1 table, companion python package pathpy
available on gitHu
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
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