238,015 research outputs found
Analysis of group evolution prediction in complex networks
In the world, in which acceptance and the identification with social
communities are highly desired, the ability to predict evolution of groups over
time appears to be a vital but very complex research problem. Therefore, we
propose a new, adaptable, generic and mutli-stage method for Group Evolution
Prediction (GEP) in complex networks, that facilitates reasoning about the
future states of the recently discovered groups. The precise GEP modularity
enabled us to carry out extensive and versatile empirical studies on many
real-world complex / social networks to analyze the impact of numerous setups
and parameters like time window type and size, group detection method,
evolution chain length, prediction models, etc. Additionally, many new
predictive features reflecting the group state at a given time have been
identified and tested. Some other research problems like enriching learning
evolution chains with external data have been analyzed as well
Analysis of group evolution prediction in complex networks.
In the world, in which acceptance and the identification with social communities are highly desired, the ability to predict the evolution of groups over time appears to be a vital but very complex research problem. Therefore, we propose a new, adaptable, generic, and multistage method for Group Evolution Prediction (GEP) in complex networks, that facilitates reasoning about the future states of the recently discovered groups. The precise GEP modularity enabled us to carry out extensive and versatile empirical studies on many real-world complex / social networks to analyze the impact of numerous setups and parameters like time window type and size, group detection method, evolution chain length, prediction models, etc. Additionally, many new predictive features reflecting the group state at a given time have been identified and tested. Some other research problems like enriching learning evolution chains with external data have been analyzed as well
Using Machine Learning to Predict the Evolution of Physics Research
The advancement of science as outlined by Popper and Kuhn is largely
qualitative, but with bibliometric data it is possible and desirable to develop
a quantitative picture of scientific progress. Furthermore it is also important
to allocate finite resources to research topics that have growth potential, to
accelerate the process from scientific breakthroughs to technological
innovations. In this paper, we address this problem of quantitative knowledge
evolution by analysing the APS publication data set from 1981 to 2010. We build
the bibliographic coupling and co-citation networks, use the Louvain method to
detect topical clusters (TCs) in each year, measure the similarity of TCs in
consecutive years, and visualize the results as alluvial diagrams. Having the
predictive features describing a given TC and its known evolution in the next
year, we can train a machine learning model to predict future changes of TCs,
i.e., their continuing, dissolving, merging and splitting. We found the number
of papers from certain journals, the degree, closeness, and betweenness to be
the most predictive features. Additionally, betweenness increases significantly
for merging events, and decreases significantly for splitting events. Our
results represent a first step from a descriptive understanding of the Science
of Science (SciSci), towards one that is ultimately prescriptive.Comment: 24 pages, 10 figures, 4 tables, supplementary information is include
Probabilistic Approach to Structural Change Prediction in Evolving Social Networks
We propose a predictive model of structural
changes in elementary subgraphs of social network based on
Mixture of Markov Chains. The model is trained and verified
on a dataset from a large corporate social network analyzed
in short, one day-long time windows, and reveals distinctive
patterns of evolution of connections on the level of local
network topology. We argue that the network investigated in
such short timescales is highly dynamic and therefore immune
to classic methods of link prediction and structural analysis,
and show that in the case of complex networks, the dynamic
subgraph mining may lead to better prediction accuracy. The
experiments were carried out on the logs from the Wroclaw
University of Technology mail server
Predicting Community Evolution in Social Networks
Nowadays, sustained development of different social media can be observed
worldwide. One of the relevant research domains intensively explored recently
is analysis of social communities existing in social media as well as
prediction of their future evolution taking into account collected historical
evolution chains. These evolution chains proposed in the paper contain group
states in the previous time frames and its historical transitions that were
identified using one out of two methods: Stable Group Changes Identification
(SGCI) and Group Evolution Discovery (GED). Based on the observed evolution
chains of various length, structural network features are extracted, validated
and selected as well as used to learn classification models. The experimental
studies were performed on three real datasets with different profile: DBLP,
Facebook and Polish blogosphere. The process of group prediction was analysed
with respect to different classifiers as well as various descriptive feature
sets extracted from evolution chains of different length. The results revealed
that, in general, the longer evolution chains the better predictive abilities
of the classification models. However, chains of length 3 to 7 enabled the
GED-based method to almost reach its maximum possible prediction quality. For
SGCI, this value was at the level of 3 to 5 last periods.Comment: Entropy 2015, 17, 1-x manuscripts; doi:10.3390/e170x000x 46 page
Neutral theory of chemical reaction networks
To what extent do the characteristic features of a chemical reaction network
reflect its purpose and function? In general, one argues that correlations
between specific features and specific functions are key to understanding a
complex structure. However, specific features may sometimes be neutral and
uncorrelated with any system-specific purpose, function or causal chain. Such
neutral features are caused by chance and randomness. Here we compare two
classes of chemical networks: one that has been subjected to biological
evolution (the chemical reaction network of metabolism in living cells) and one
that has not (the atmospheric planetary chemical reaction networks). Their
degree distributions are shown to share the very same neutral
system-independent features. The shape of the broad distributions is to a large
extent controlled by a single parameter, the network size. From this
perspective, there is little difference between atmospheric and metabolic
networks; they are just different sizes of the same random assembling network.
In other words, the shape of the degree distribution is a neutral
characteristic feature and has no functional or evolutionary implications in
itself; it is not a matter of life and death.Comment: 13 pages, 8 figure
The Lifecycle and Cascade of WeChat Social Messaging Groups
Social instant messaging services are emerging as a transformative form with
which people connect, communicate with friends in their daily life - they
catalyze the formation of social groups, and they bring people stronger sense
of community and connection. However, research community still knows little
about the formation and evolution of groups in the context of social messaging
- their lifecycles, the change in their underlying structures over time, and
the diffusion processes by which they develop new members. In this paper, we
analyze the daily usage logs from WeChat group messaging platform - the largest
standalone messaging communication service in China - with the goal of
understanding the processes by which social messaging groups come together,
grow new members, and evolve over time. Specifically, we discover a strong
dichotomy among groups in terms of their lifecycle, and develop a separability
model by taking into account a broad range of group-level features, showing
that long-term and short-term groups are inherently distinct. We also found
that the lifecycle of messaging groups is largely dependent on their social
roles and functions in users' daily social experiences and specific purposes.
Given the strong separability between the long-term and short-term groups, we
further address the problem concerning the early prediction of successful
communities. In addition to modeling the growth and evolution from group-level
perspective, we investigate the individual-level attributes of group members
and study the diffusion process by which groups gain new members. By
considering members' historical engagement behavior as well as the local social
network structure that they embedded in, we develop a membership cascade model
and demonstrate the effectiveness by achieving AUC of 95.31% in predicting
inviter, and an AUC of 98.66% in predicting invitee.Comment: 10 pages, 8 figures, to appear in proceedings of the 25th
International World Wide Web Conference (WWW 2016
Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network
The network of patents connected by citations is an evolving graph, which
provides a representation of the innovation process. A patent citing another
implies that the cited patent reflects a piece of previously existing knowledge
that the citing patent builds upon. A methodology presented here (i) identifies
actual clusters of patents: i.e. technological branches, and (ii) gives
predictions about the temporal changes of the structure of the clusters. A
predictor, called the {citation vector}, is defined for characterizing
technological development to show how a patent cited by other patents belongs
to various industrial fields. The clustering technique adopted is able to
detect the new emerging recombinations, and predicts emerging new technology
clusters. The predictive ability of our new method is illustrated on the
example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of
patents is determined based on citation data up to 1991, which shows
significant overlap of the class 442 formed at the beginning of 1997. These new
tools of predictive analytics could support policy decision making processes in
science and technology, and help formulate recommendations for action
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