报告人：堵丁柱教授，美国德克萨斯大学达拉斯分校

报告地点：20—200

报告题目1： Adaptive Influence Maximization: Adaptability via Non-adaptability

报告摘要：Adaptive influence maximization is a hot research topic in social network which has attracted many researchers' attention. To enhance the role of adaptability, new information diffusion models, such as dynamic independent cascade model, are proposed. In this talk, the speaker will present a recent discovery that in some models, the adaptive influence maximization can be transformed into a non-adaptive problem in another modeL. This reveals an interesting relationship between Adaptability and Non-adaptability.

报告时间：2023年11月16日（星期四）16:20-17:20

报告题目2：Robust rumor blocking problem with uncertain rumor sources in social networks

报告摘要：Rumormongers spread negative information throughout the social network, which may even lead to panic or unrest. Rumor should be blocked by spreading positive information from several protector nodes in the network. Users will not be influenced if they receive the positive information ahead of negative one. In many cases, network manager or government may not know the exact positions where rumor will start. Meanwhile, protector nodes also need to be selected in order to prepare for rumor blocking. Given a social network G = (V,E,P), where P is the weight function on edge set E, P(u,v) is the probability that v is activated by u after u is activated. Assume there will be l rumormongers in the network while the exact positions are not clear, Robust Rumor Blocking (RRB) problem is to select k nodes as protector such that the expected eventually influenced users by rumor is minimized. RRB will be proved to be \Sigma^p_2-complete and its approximation will be discussed.

报告时间：2023年11月17日（星期五）9:40-10:40

报告题目3：Revenue Maximization in Viral Marketing with Recommendations

报告摘要：When you purchase a product $A$ in an online store, a recommendation could come out One who buys $A$ may also like to buy $B$. The recommendation is generated from analysis on historical data. Consider a directed social network $G = (V ,E)$ with independent cascade (IC) model, i.e., each arc $(u, v)$ is associated with a probability $p_{uv}$. This means that node $v$ accepts influence of $u$ with probability $p_{uv}$. Let $g_1, g_2, . . . , g_k$ be $k$ products with prices $c_1, c_2, . . . , c_k$, respectively. Suppose that a customer $u$ who buys product $g_i$ would also buy product $g_j$ with probability $p^g_{ij}$. In this paper, we study the following problem: Given a budget $B$, find a set of customers for giving certain discount within the budget $B$ to maximize the expected total revenue.

报告时间：2023年11月20日（星期一）10:30-11:30

报告人简介：

堵丁柱教授，1982年获中国科学院硕士学位，1985年获美国加利福尼亚大学圣芭芭拉分校博士学位。曾任职于美国伯克利数学科学研究所（博士后），麻省理工大学数学系（助理教授），中国科学院应用数学所（研究员），普林斯顿大学计算机系（访问学者），明尼苏达大学计算机系（教授），西安交通大学理学院（院长），香港城市大学（教授），韩国高丽大学（世界级教授）。并于2002-2005任美国国家基金会计算机理论项目主管。现任德克萨斯大学达拉斯分校计算机系教授。研究方向包括组合优化，计算机网络和计算复杂性理论。发表论文240多篇，著书10余本。现为《离散数学、算法和应用》的主编，以及15多个杂志的编委。1998年获得美国INFORMS的CSTS奖，1993年获得中国自然科学二等奖，1992年获得中国科学院自然科学一等奖。

邀请人：张昭