Conditional Reliability in Uncertain Graphs

Network reliability is a well-studied problem that requires to measure the probability that a target node is reachable from a source node in a probabilistic (or uncertain) graph, i.e., a graph where every edge is assigned a probability of existence. Many approaches and problem variants have been considered in the literature, all assuming that edge-existence probabilities are fixed. Nevertheless, in real-world graphs, edge probabilities typically depend on external conditions. In metabolic networks a protein can be converted into another protein with some probability depending on the presence of certain enzymes. In social influence networks the probability that a tweet of some user will be re-tweeted by her followers depends on whether the tweet contains specific hashtags. In transportation networks the probability that a network segment will work properly or not might depend on external conditions such as weather or time of the day. In this paper we overcome this limitation and focus on conditional reliability, that is assessing reliability when edge-existence probabilities depend on a set of conditions. In particular, we study the problem of determining the k conditions that maximize the reliability between two nodes. We deeply characterize our problem and show that, even employing polynomial-time reliability-estimation methods, it is NP-hard, does not admit any PTAS, and the underlying objective function is non-submodular. We then devise a practical method that targets both accuracy and efficiency. We also study natural generalizations of the problem with multiple source and target nodes. An extensive empirical evaluation on several large, real-life graphs demonstrates effectiveness and scalability of the proposed methods.Comment: 14 pages, 13 figure

Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure

A reliability-based approach for influence maximization using the evidence theory

The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc

Topic-Aware Influence Maximization Framework

H διπλωματική εργασία εστιάζει στην ανάπτυξη ενός νέου πλαισίου για την αντιμετώπιση του προβλήματος της μεγιστοποίησης με επίγνωση θέματος. Το πρόβλημα παρουσιάζει υψηλή υπολογιστική πολυπλοκότητα και ανήκει στην περιοχή της κοινωνικής επιρροής η οποία περιλαμβάνει τόσο ψυχολογικές όσο και κοινωνιολογικές πτυχές. Συγκεκριμένα, το πρόβλημα μεγιστοποίησης επιρροής στοχεύει στην εύρεση ενός υποσυνόλου από κόμβους που μεγιστοποιούν την αναμενόμενη διάδοση επιρροής σε ένα δίκτυο. Λόγω της NP-hard υπολογιστικής πολυπλοκότητας, οι προσεγγιστικοί αλγόριθμοι είναι επιτακτικοί για την εύρεση της βέλτιστης ή σχεδόν βέλτιστης λύσης με μικρή υπολογιστική προσπάθεια. Επιπλέον, θα πρέπει να χρησιμοποιούνται πραγματικά δίκτυα για να παρέχουν έγκυρα αποτελέσματα και σημαντικές πληροφορίες σχετικά με το τρόπο διάδοσης της κοινωνικής επιρροής αποφεύγοντας έτσι λάθη και προβληματικά συμπεράσματα. Στο πλαίσιο αυτής της διπλωματικής εργασίας, το πρόβλημα της μεγιστοποίησης επιρροής με επίγνωση θέματος μελετήθηκε και ένα νέο πλαίσιο βασισμένο στον αλγόριθμο Hyperlink Induced Topic Search (HITS) εισήχθη. Η προτεινόμενη προσεγγιστική λύση αποτελείται από δύο μέρη, δηλαδή τον αλγόριθμο HITS και τους αλγορίθμους Greedy ή Cost Effective Lazy Forward (CELF) με τροποποιημένο το μοντέλο Independent Cascade ώστε να βελτιωθούν τα αποτελέσματα της επιρροής με επίγνωση θέματος. Ο αλγόριθμος HITS στοχεύσει να αναλύει συνδέσεις και οι αλγόριθμοι Greedy και CELF να βρίσκουν τους κόμβους που μεγιστοποιούν την αναμενόμενη διάδοση επιρροής σε ένα δίκτυο. Για την αξιολόγηση, η Neo4j βάση δεδομένων γράφου χρησιμοποιήθηκε σαν πλατφόρμα για τα εμπειρικά πειράματα. Επιπλέον, σύνολα δεδομένων από το κοινωνικό δίκτυο Yelp μαζί με δημιουργούμενους γράφους βασισμένους στο μοντέλο Barabási–Albert παρέχουν τα απαραίτητα δεδομένα για δοκιμή. Τα υπολογιστικά πειράματα απεικονίζουν την συνάφεια των αναπτυγμένων αλγορίθμων και υπογραμμίζουν το ρόλο των μερών του προτεινόμενου πλαισίου. Τέλος, αξίζει να σημειωθεί ότι οι αναπτυγμένοι αλγόριθμοι Greedy και CELF αυτής της διπλωματικής εργασίας έχουν γίνει συνεισφορά στη Neo4j κοινότητα ανοιχτού κώδικα.The focus of the thesis is given on the development of a novel framework to deal with the topic-aware influence maximization problem. The problem appears high computational complexity and belong in the area of social influence which involves both psychological and sociological aspects. In particular, the influence maximization problem aims to find a subset of nodes that maximize the expected spread of influence in a network. Due to NP-hard computational complexity, the approximation algorithms are imperative to gain optimal or near-optimal solution with minor computational effort. In addition, real life networks should be used to provide valid results and significant information about how social influence is spread avoiding errors and problematic conclusions. In the context of this thesis, the topic-aware influence maximization problem is studied and a novel framework based on the Hyperlink Induced Topic Search (HITS) algorithm is introduced. The proposed solution approach consists of two main components, i.e., the HITS algorithm and the Greedy or Cost Effective Lazy Forward (CELF) algorithms with modified Independent Cascade model in order to improve the results of the influence according to a topic. The HITS algorithm aims to analyze links and the Greedy and CELF algorithms to find the nodes that maximize the expected spread of influence in a network. For the evaluation, the Neo4j graph database is used as a platform for the empirical experiments. Moreover, datasets of the Yelp social network alongside with generated graphs based on Barabási–Albert model provide the necessary data for testing. The computational results illustrate the pertinence of the developed algorithms and underline the role of the proposed framework’s components. Finally, it is worth to mention that the developed Greedy and CELF algorithms of this thesis have been contributed to the Neo4j open-source community

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page